Oracle® Utilities Data Model

Implementation and Operations Guide

Release 11.4.1

E48730-01

November 2013

Oracle Utilities Data Model Implementation and Operations Guide, Release 11.4.1

E48730-01

Copyright © 2013, Oracle and/or its affiliates. All rights reserved.

Primary Author: Thomas Van Raalte

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Contents

List of Examples

List of Figures

Preface

- Audience

- Documentation Accessibility

- Related Oracle Resources

- Conventions

1 Introduction to Oracle Utilities Data Model Customization

- What is the Oracle Utilities Data Model?

- Components of Oracle Utilities Data Model

- Oracle Products That Make Up Oracle Utilities Data Model

- Steps for Implementing an Oracle Utilities Data Model Warehouse

- Before You Begin Customizing the Oracle Utilities Data Model

- Prerequisite Knowledge for Implementers

- Responsibilities of a Data Warehouse Governance Committee

- Performing Fit-Gap Analysis for Oracle Utilities Data Model

- Data Encryption and Security for Oracle Utilities Data Model

2 Physical Model Customization

- Characteristics of the Default Physical Model

- Customizing the Oracle Utilities Data Model Physical Model

- Questions to Answer Before You Customize the Physical Model

- Conventions When Customizing the Physical Model

- Foundation Layer Customization

- Common Change Scenarios

- General Recommendations When Designing Physical Structures

- Tablespaces in the Oracle Utilities Data Model

- Data Compression in the Oracle Utilities Data Model

- Types of Data Compression Available

- Tables for Supertype and Subtype Entities in Oracle Utilities Data Model

- Surrogate Keys in the Physical Model

- Integrity Constraints in Oracle Utilities Data Model

- Indexes and Partitioned Indexes in the Oracle Utilities Data Model

- Partitioned Tables in the Oracle Utilities Data Model

- Partitioning the Oracle Utilities Data Model for Manageability

- Partitioning the Oracle Utilities Data Model for Easier Data Access

- Partitioning the Oracle Utilities Data Model for Join Performance

- Parallel Execution in the Oracle Utilities Data Model

- Enabling Parallel Execution for a Session

- Enabling Parallel Execution of DML Operations

- Enabling Parallel Execution at the Table Level

3 Access Layer Customization

- Introduction to Customizing the Access Layer of Oracle Utilities Data Model

- Derived Tables in the Oracle Utilities Data Model

- Creating New Derived Tables for Calculated Data

- Customizing Oracle Utilities Data Model Data Mining Models

- Creating a New Data Mining Model for Oracle Utilities Data Model

- Modifying Oracle Utilities Data Model Data Mining Models

- Aggregate Tables in the Oracle Utilities Data Model

- Dimensional Components in the Oracle Utilities Data Model

- Characteristics of a Dimensional Model

- Characteristics of Relational Star and Snowflake Tables

- Declaring Relational Dimension Tables

- Validating Relational Dimension Tables

- Characteristics of the OLAP Dimensional Model

- Oracle OLAP Cube Views

- Cube Materialized Views

- Characteristics of the OLAP Cubes in Oracle Utilities Data Model

- Defining New Oracle OLAP Cubes for Oracle Utilities Data Model

- Changing an Oracle OLAP Cube in Oracle Utilities Data Model

- Creating a Forecast Cube for Oracle Utilities Data Model

- Choosing a Cube Partitioning Strategy for Oracle Utilities Data Model

- Choosing a Cube Data Maintenance Method for Oracle Utilities Data Model

- Materialized Views in the Oracle Utilities Data Model

- Types of Materialized Views and Refresh Options

- Refresh Options for Materialized Views with Aggregates

- Refresh Options for Materialized Views Containing Only Joins

- Refresh Options for Nested Materialized Views

- Choosing Indexes for Materialized Views

- Partitioning and Materialized Views

- Compressing Materialized Views

4 ETL Implementation and Customization

- The Role of ETL in the Oracle Utilities Data Model

- ETL for the Foundation Layer of an Oracle Utilities Data Model Warehouse

- Writing Your Own Source-ETL

- Source-ETL Design Considerations

- ETL Architecture for Oracle Utilities Data Model Source-ETL

- Creating a Source to Target Mapping Document for the Source-ETL

- Designing a Plan for Rectifying Source-ETL Data Quality Problems

- Designing Source-ETL Workflow and Jobs Control

- Designing Source-ETL Exception Handling

- Writing Source-ETL that Loads Efficiently

- Customizing Intra-ETL for Oracle Utilities Data Model

- Executing Derived Intra-ETL Programs

- Refreshing Aggregate Materialized Views

- Refreshing OLAP Cubes

- Executing Intra-ETL Workflow

- Performing an Initial Load of an Oracle Utilities Data Model Warehouse

- Performing an Initial Load of the Foundation Layer

- Performing an Initial Load of the Access Layer

- Executing the Default Oracle Utilities Data Model Intra-ETL

- Refreshing the Data in an Oracle Utilities Data Model Warehouse

- Refreshing the Foundation Layer of Oracle Utilities Data Model Warehouse

- Refreshing the Access Layer of an Oracle Utilities Data Model Warehouse

- Refreshing Oracle Utilities Data Model Derived Tables

- Refreshing Oracle Utilities Data Model Aggregate Materialized Views

- Refreshing Oracle Utilities Data Model OLAP Cubes

- Refreshing Oracle Utilities Data Model Data Mining Models

- Managing Errors During Oracle Utilities Data Model Intra-ETL Execution

- Monitoring the Execution of the Intra-ETL Process

- Recovering an Intra ETL Process

5 Report and Query Customization

- Reporting Approaches in Oracle Utilities Data Model

- Customizing Oracle Utilities Data Model Sample Reports

- Writing Your Own Queries and Reports

- Optimizing Star Queries

- Troubleshooting Oracle Utilities Data Model Report Performance

- Writing As Is and As Was Queries

- Characteristics of an As Is Query

- Characteristics of an As Was Query

- Examples: As Is and As Was Queries Against Oracle Utilities Data Model

- Tutorial: Creating a New Oracle Utilities Data Model Dashboard

- Tutorial: Creating a New Oracle Utilities Data Model Report

6 Metadata Collection and Reports

- Overview of Managing Metadata for Oracle Utilities Data Model

- Metadata Categories and Standards

- Working with a Metadata Repository

- Browsing Metadata Reports and Dashboard

- Using the Measure-Entity Tab Business Areas and Measures Attributes and Entities

- Using the Entity-Measure Tab Entity to Attribute Measures

- Using the Program-Table Tab

- Using the Table-Program Tab

- Collecting and Populating Metadata

- Load LDM/PDM Metadata (Table MD_ENTY)

- GIVE_ABBRV

- MD_DM_ALL_ENT_ATTR

- PL/SQL Program to Update Column Name

- PL/SQL program to insert initial data into MD_OIDM_ATTR_COL_NAM

- PL/SQL program to load data into MD_ENTY

- Load Program (Intra-ETL) Metadata (Table MD_PRG)

- Load Reports and KPI Metadata (Table MD_KPI and MD_REF_ENTY_KPI):

A Sizing and Configuring an Oracle Utilities Data Model Warehouse

- Sizing an Oracle Utilities Data Model Warehouse

- Configuring a Balanced System for Oracle Utilities Data Model

- Maintaining High Throughput in an Oracle Utilities Data Model Warehouse

- Configuring I/O in an Oracle Utilities Data Model for Bandwidth not Capacity

- Planning for Growth of Your Oracle Utilities Data Model

- Testing the I/O System Before Building the Warehouse

- Balanced Hardware Configuration Guidelines for Oracle Utilities Data Model

Index

List of Examples

- 2-1 Creating a Compressed Table for Oracle Utilities Data Model

- 3-1 Adding a New Column to a Mining Model in Oracle Utilities Data Model

- 4-1 Using Exchange Partition Statement with a Partitioned Table

- 5-1 Creating a Relational Query for Oracle Utilities Data Model

- 5-2 Star Transformation

- 5-3 Troubleshooting an Oracle Utilities Data Model Report

- 5-4 Troubleshooting a Report: A Table Does Not Exist

- 5-5 Troubleshooting a Report: When the Database is Not Connected

- 5-6 As Is Query for Tax Collection Split by Marital Status

- 5-7 As Was Queries for Tax Collection Split by Marital Status

- 5-8 As Is Query for Tax Collection Data Split by County

- 5-9 As Was Queries for Tax Collection Data Split by County

List of Figures

- 1-1 Data Warehouse Reference Architecture with Oracle Utilities Data Model

- 2-1 Layers of an Oracle Utilities Data Model Warehouse

- 2-2 Partitioning for Join Performance

- 2-3 Parallel Execution of a Full Partition-Wise Join Between Two Tables

- 2-4 Partial Partition-Wise Join

- 3-1 Star Schema Diagram

- 3-2 Diagram of the OLAP Dimensional Model

- 4-1 ETL Flow Diagram

- 4-2 Oracle Utilities Data Model Intra-ETL Workflow

Preface

The Oracle Utilities Data Model Implementation and Operations Guide describes best practices for implementing a data warehouse based on the Oracle Utilities Data Model.

This preface contains the following topics:

- Audience

- Documentation Accessibility

- Related Oracle Resources

- Conventions

Audience

This document is intended for business analysts, data modelers, data warehouse administrators, IT staff, and ETL developers who implement an Oracle Utilities Data Model warehouse.

Documentation Accessibility

For information about Oracle's commitment to accessibility, visit the Oracle Accessibility Program website at http://www.oracle.com/pls/topic/lookup?ctx=acc&id=docacc.

Access to Oracle Support

Oracle customers have access to electronic support through My Oracle Support. For information, visit http://www.oracle.com/pls/topic/lookup?ctx=acc&id=info or visit http://www.oracle.com/pls/topic/lookup?ctx=acc&id=trs if you are hearing impaired.

Related Oracle Resources

Oracle provides many resources for you when implementing the Oracle Utilities Data Model.

Oracle Utilities Data Model Documentation Set

For more information on Oracle Utilities Data Model, see the following documents in the Oracle Utilities Data Model Release 11g documentation set:

- Oracle Utilities Data Model Installation Guide

- Oracle Utilities Data Model Reference

- Oracle Utilities Data Model Release Notes

Oracle Technology Network

Visit the Oracle Technology Network (OTN to access to demos, whitepapers, Oracle By Example (OBE) tutorials, updated Oracle documentation, and other collateral.

Registering on OTN

You must register online before using OTN, Registration is free and can be done at

www.oracle.com/technetwork/index.html

Oracle Documentation on OTN

The Oracle Documentation site on OTN provides access to Oracle documentation. After you have a user name and password for OTN, you can go directly to the documentation section of the OTN Web site at

www.oracle.com/technetwork/indexes/documentation/index.html

Oracle Learning Library on OTN

The Oracle Learning Library provides free online training content (OBEs, Demos and Tutorials). After you have a user name and password for OTN, you can go directly to the Oracle Learning Library Web site at

www.oracle.com/technetwork/tutorials/index.html

Then you can search for the tutorial or demo (within "All") by name.

Conventions

The following text conventions are used in this document:

Convention

Meaning

boldface

Boldface type indicates graphical user interface elements associated with an action, or terms defined in text or the glossary.

italic

Italic type indicates book titles, emphasis, or placeholder variables for which you supply particular values.

monospace

Monospace type indicates commands within a paragraph, URLs, code in examples, text that appears on the screen, or text that you enter.

1 Introduction to Oracle Utilities Data Model Customization

This chapter provides an introduction to customizing Oracle Utilities Data Model. It contains the following topics:

- What is the Oracle Utilities Data Model?

- Steps for Implementing an Oracle Utilities Data Model Warehouse

- Before You Begin Customizing the Oracle Utilities Data Model

- Performing Fit-Gap Analysis for Oracle Utilities Data Model

- Data Encryption and Security for Oracle Utilities Data Model

What is the Oracle Utilities Data Model?

Oracle Utilities Data Model leverages several Oracle Database data warehouse and Business Intelligence concepts that need to be clarified to understand the structure and use of Oracle Utilities Data Model.

Oracle Utilities Data Model provides "One Single True Vision of the Business". This unique architecture provides the utilities Service Provider (CSP) Flexibility, Agility, Scalability and Accuracy to obtain a real competitive advantage.

A typical enterprise data warehouse architecture, as shown in Figure 1-1, is composed of several layers ordered by the growing actionable value of the information in the warehouse:

- The Data Source layer (operational systems, Commercial-Off-The shelf solution, unstructured and syndicated data, with possibly a Master Data Management system).

- The Staging layer: Typically used for transformation and data cleansing. It is also sometimes used as Operational Data Store, in particular for real-time operational reporting.

- The Foundation layer: It is typically used to store all transactions and reference data at the most atomic level. Best practices require that this level is 3rd normal form, to avoid data redundancy.

- The Access and Performance or Analytical layer: this is the layer optimized for the business end-users. It usually contains the star schema to answer business questions, as well as OLAP tools and mining models.

- The Information (or Information Access) layer: This is the metadata layer and above, accessed by end-users through their Business Intelligence and/or reporting tools, or even external analytical tools (other OLAP or Mining tools). This layer is usually changeable by normal end-users (within their roles and responsibility). This is where the performance management applications provide their reports, where user roles, alerts, guided analytics, dashboards and reports are defined (usually by a specific BI administrator).

- The data movement from one layer to the other is run through ETL / ELT tools. One distinguishes the standard ETL/ELT (from data sources to foundation layer) from the intra-ETLs (from foundation layer up to the reporting).

Figure 1-1 Data Warehouse Reference Architecture with Oracle Utilities Data Model

Within a standard enterprise data warehouse architecture, as shown in Figure 1-1, if an adapter is used, for example DataRaker, the Staging area is also provided. Oracle Utilities Data Model covers Foundation Layer, plus the intra-ETL part, and includes parts of the reporting area if OBIEE is used (Oracle Utilities Data Model also includes the pre-built OBIEE repository).

Components of Oracle Utilities Data Model

Oracle Utilities Data Model includes the following components:

- Logical model which is a third normal form (3NF) entity-object standards-based model. The logical model is described in Oracle Utilities Data Model Reference.

- Physical model defined as one Oracle Database schema. This schema defines all the relational, OLAP, and data mining components.

- Intra-ETL database packages and SQL scripts to extract, transform, and load (ETL) data from the Oracle Utilities Data Model 3NF physical tables to the derived and aggregate tables in Oracle Utilities Data Model.

- Sample reports and dashboards developed using Oracle Business Intelligence Suite Enterprise Edition.

- DDL and installation scripts

Note:
When you use the Oracle Installer to install Oracle Utilities Data Model, you have the choice of performing two different types of installations:

- Installation of the Oracle Utilities Data Model component, itself

- Installation of sample reports (and schemas)

See Oracle Utilities Data Model Installation Guide for detailed information on the different types of installation.

See:
Oracle Utilities Data Model Reference for detailed descriptions of the components.

Oracle Products That Make Up Oracle Utilities Data Model

Several Oracle technologies are involved in building the infrastructure for Oracle Utilities Data Model:

- Oracle Database with OLAP, Data Mining and Partitioning Option

- Oracle Development Tools

- Oracle Business Intelligence Suite Enterprise Edition Presentation Tools

Oracle Database with OLAP, Data Mining and Partitioning Option

Oracle Utilities Data Model uses a complete Oracle technical stack. It leverages the following data warehousing features of the Oracle Database: SQL model, compression, partitioning, advanced statistical functions, materialized views, data mining, and online analytical processing (OLAP).

Oracle Development Tools

You can use the following Oracle tools to customize the predefined physical models provided with Oracle Utilities Data Model, or to populate the target relational tables and materialized cube views.

Table 1-1 Oracle Development Tools Used with Oracle Utilities Data Model

Name

Use

SQL Developer or SQL*Plus

To modify, customize, and extend database objects

Analytic Workspace Manager

To view, create, develop, and manage OLAP dimensional objects.

Oracle Business Intelligence Suite Enterprise Edition Presentation Tools

Oracle Business Intelligence Suite Enterprise Edition is a comprehensive suite of enterprise BI products that delivers a full range of analysis and reporting capabilities. You can use Oracle Business Intelligence Suite Enterprise Edition Answers and Dashboard presentation tools to customize the predefined sample dashboard reports that are provided with Oracle Utilities Data Model.

See:
"Reporting Approaches in Oracle Utilities Data Model".

Steps for Implementing an Oracle Utilities Data Model Warehouse

Although Oracle Utilities Data Model was designed following best practices for utilities service providers, usually the model requires some customization to meet your business needs.

The reasons that you might customize Oracle Utilities Data Model include:

- Your business does not have a business area that is included in the Oracle Utilities Data Model.

- You must apply a table or column, or change a calculation or business rule in the Intra-ETL due to the unique way your company does business.

Typical physical model modifications include: adding, deleting, modifying, or renaming tables and columns; or altering foreign keys, constraints, or indexes.

To implement an Oracle Utilities Data Model warehouse, take the following steps:

1. Perform the organizational tasks outlined in "Before You Begin Customizing the Oracle Utilities Data Model".

2. Create a fit-gap analysis report by following the process outlined "Performing Fit-Gap Analysis for Oracle Utilities Data Model".

3. In a development environment, install a copy of the Oracle Utilities Data Model.

4. Customize Oracle Utilities Data Model by making the changes you documented in the fit-gap analysis report. Make the changes in the following order:

1. Foundation layer of the physical model and the ETL to populate that layer. When customizing the physical objects, follow the guidelines in "Foundation Layer Customization". When writing the ETL, follow the guidelines in "ETL for the Foundation Layer of an Oracle Utilities Data Model Warehouse".

2. Access layer of the physical model and the ETL to populate that layer. When designing the physical objects, follow the guidelines in Chapter 3, "Access Layer Customization" When writing the ETL, follow the guidelines in "Customizing Intra-ETL for Oracle Utilities Data Model".

5. In a test environment, make a copy of your customized version of Oracle Utilities Data Model. Then, following the documentation you created in Step 2, test the customized version of Oracle Utilities Data Model

6. Following your typical procedures, roll the tested customized version of Oracle Utilities Data Model out into pre-production and, then, production.

Tip:
Keep 'clean' copies of the components delivered with Oracle Utilities Data Model components. This is important when upgrading to later versions of Oracle Utilities Data Model.

Before You Begin Customizing the Oracle Utilities Data Model

Before you begin customizing Oracle Utilities Data Model, ensure the following teams and committees exist:

- Data warehouse governance steering committee. This steering committee has the responsibilities outlined in "Responsibilities of a Data Warehouse Governance Committee".

- Implementation team. This team consists of IT engineers who have the expertise outlined in "Prerequisite Knowledge for Implementers". This team has the responsibilities outlined in "Steps for Implementing an Oracle Utilities Data Model Warehouse".

- Fit-gap analysis team. This team consists of business analysts who can identify the business requirements and scope of the Oracle Utilities Data Model and at least some engineers in the Implementation team. Business members of this team must understand logical data modeling so that they can evaluate what changes must be made to the foundation and access layers of the physical model. This team has the responsibilities outlined in "Performing Fit-Gap Analysis for Oracle Utilities Data Model".

After these teams and committees are formed:

- Discuss the approach and determine the involvement and roles of every party involved in the customization (for example, business and IT).

- Agree on the scope of the project (that is, agree on what new data must be in the data warehouse and why it is needed). The order of implementation either top-down (per business or subject area) or bottom-up (source-leading) should be based on the "quick wins" (easy implementation, clean and known source, no or very little changes, out-of-the-box reports), themselves ordered by business relevance (from a Return On Investment perspective and from a strategic perspective).

- Agree on the timing and the working arrangements.

Prerequisite Knowledge for Implementers

As outlined in "Oracle Products That Make Up Oracle Utilities Data Model", the Oracle Utilities Data Model uses much of the Oracle stack. Consequently, to successfully implement the Oracle Utilities Data Model, the implementation team needs:

- Experience performing information and data analysis and data modeling. (Experience using Oracle SQL Data Modeler, is a plus).

- Hands on experience developing ETL or ELT, preferable in the chosen ETL tool (ODI, Golden Gate, and so on).

- Knowledge of the source applications, their data and their table structures from which you want to load data into Oracle Utilities Data Model.

- An understanding of the Oracle technology stack, especially data warehouse (Database, Data Warehouse, OLAP, Data Mining, Oracle Business Intelligence Suite Enterprise Edition).

- Hands-on experience using:

- Oracle Database

- PL/SQL

- SQL DDL and DML syntax

- Analytic Workspace Manager

- Oracle SQL Developer

- Oracle Business Intelligence Suite Enterprise Edition Administrator, Answers, and Dashboards

Responsibilities of a Data Warehouse Governance Committee

Governance is of concern to any enterprise, executive team or individual with an interest in the processes, standards, and compliance. It is even more important to organizations that have invested in data warehousing.

Data warehouse governance occurs within the context of overall IT governance. It provides the necessary policies, process and procedures, which must be clearly communicated to the entire corporation, from the IT employees to the front-end operational personnel.

Before you customize Oracle Utilities Data Model, setup a data warehouse governance steering committee if one does not exist. The role of this steering committed is to oversee the data warehouse to provide an environment that reaches across the enterprise and drives the best business value.

Data Warehouse Governance Committee: Overall Responsibilities

The data warehouse governance steering committee sets direction and response for the governance framework and should at least cover the following areas:

- The entire data warehouse life cycle.

- Agree on the data to process and make available to end-users.

- Determine what is the minimum quality criteria for the data that is available to end users and determine how to measure and analyze these criteria against the quality of the data that is the source data for the data warehouse.

- The business goals of the organization to apply core information from data warehouse.

- The policies, procedures and standards for data resource and data access, and the implications it may have on the existing or future business processes. For the later, the committee must make sure to communicate early enough to the right persons the process change request to ease the integration and to save time.

- The life cycle of data warehouse component management.

Data Warehouse Governance Committee: Data Governance Responsibilities

The more detailed focus in data warehouse governance is data governance. Data governance tasks include:

- Approving the data modeling standards, metadata standards and other related standards. This includes determining a metadata strategy as discussed in "Overview of Managing Metadata for Oracle Utilities Data Model" and identifying the data modeling tools to use that support these standards.

- Determining the data retention policy.

- Designing a data access policy based on legal restrictions and data security rules.

- Designing a data backup strategy that aligns with the impact analysis to the business unit.

- Monitoring and reporting on data usage, activity, and alerts.

Performing Fit-Gap Analysis for Oracle Utilities Data Model

Fit-gap analysis is where you compare your information needs and utilities business requirements with the structure that is available with Oracle Utilities Data Model. You identify any required functionality that is not included in the model and the default schema, and other modifications that are necessary to meet your requirements.

The result of your fit-gap analysis is a customization report which is a brief explanation of the adaptations and adjustments required to customize Oracle Utilities Data Model to fit your utilities environment.

The fit-gap analysis team writes the customization report by taking the following steps:

1. If you have performed previous evaluations, review the documentation from the previous phases, and if necessary add team members with the required business and technical expertise.

2. Review the data and map the data structures of your source to the Oracle Utilities Data Model schema:

- Starting from business requirements, questions, and rules, identify any objects that are not in the Oracle Utilities Data Model.

- Compare the Oracle Utilities Data Model to your existing application model if have one.

- Compare the Oracle Utilities Data Model to the application data that you are using as a data source to the Oracle Utilities Data Model warehouse.

3. Determine the differences between your needs and Oracle Utilities Data Model schema. To help you with this task, produce a list of actions people may take with the system (examples rather than models), and create use cases for appraising the functionality of the Oracle Utilities Data Model Warehouse. Answer the following questions about the differences you find:

- Which differences you can live with, and which must be reconciled?

- What can you do about the differences you cannot live with?

4. Identify the changes you must make to the default design of Oracle Utilities Data Model to create the customized warehouse. Identify these changes in the following order:

1. Physical model. Follow the guidelines outlined in Chapter 2, "Physical Model Customization".

2. ETL mapping. Follow the guidelines outlined in Chapter 4, "ETL Implementation and Customization" to identify and design the source-ETL that you must create and to identify and make any changes to the intra-ETL provided with Oracle Utilities Data Model.

3. Reports: A clear distinction should be made between reports end-users could create themselves from the default data and data structure available in the OBIEE repository, and those that would require additions (from earlier changes in Physical model, or simply because the considered entity was not accessible by default in the repository). Note that this step could be done a bit later, in a second phase, not for the initial implementation, with the risk to possibly miss an important source of information if the business interviews were not run properly.

Tip:
When identifying changes, ensure that the changes meet your security and metadata requirements.

5. Write the customization report, detailing what changes are required to make the Oracle Utilities Data Model match your business needs. This includes any additions and changes to interfaces to existing systems.

6. Based on the customization report, update the Project Plan and perform the steps outlined in "Steps for Implementing an Oracle Utilities Data Model Warehouse".

Data Encryption and Security for Oracle Utilities Data Model

To comply with privacy and data protection requirements, Oracle Utilities Data Model is certified with Transparent Data Encryption and Oracle Database Vault.

For more information on using Transparent Data Encryption, see Oracle Database Administrator's Guide and for information on using Oracle Database Vault, see Oracle Database Vault Administrator's Guide.

For more information on these topics, see:

http://www.oracle.com/technetwork/database/options/advanced-security/index-099011.html

http://www.oracle.com/technetwork/database/options/database-vault/index-085211.html

2 Physical Model Customization

This chapter provides general information about customizing the physical model of Oracle Utilities Data Model and more detailed information about customizing the foundation layer of the physical model. This chapter contains the following topics:

- Characteristics of the Default Physical Model

- Customizing the Oracle Utilities Data Model Physical Model

- Foundation Layer Customization

- General Recommendations When Designing Physical Structures

See also:
Chapter 3, "Access Layer Customization"

Characteristics of the Default Physical Model

The default physical model of Oracle Utilities Data Model defines:

670+ tables and 4,800+ columns

1,300+ industry-specific measures and 80 KPIs

pre-built OLAP cubes and Analytical Models

The default physical model of the Oracle Utilities Data Model shares characteristics of a multischema "traditional" data warehouse, as described in "Layers in a "Traditional" Data Warehouse", but defines all data structures in a single schema as described in "Layers in the Default Oracle Utilities Data Model Warehouse".

Layers in a "Traditional" Data Warehouse

Historically, three layers are defined for a data warehouse environment:

- Staging layer. This layer is used when moving data from the operational system and other data sources into the data warehouse itself. It consists of temporary loading structures and rejected data. Having a staging layer enables the speedy extraction, transformation and loading (ETL) of data from your operational systems into data warehouse without disturbing any of the business users. It is in this layer the much of the complex data transformation and data quality processing occurs. The most basic approach for the design of the staging layer is as a schema identical to the one that exists in the source operational system.

Note:
In some implementations this layer is not necessary, because all data transformation processing is done as needed as data is extracted from the source system before it is inserted directly into the foundation layer.

- Foundation or integration layer. This layer is traditionally implemented as a Third Normal Form (3NF) schema. A 3NF schema is a neutral schema design independent of any application, and typically has many tables. It preserves a detailed record of each transaction without any data redundancy and allows for rich encoding of attributes and all relationships between data elements. Users typically require a solid understanding of the data to navigate the more elaborate structure reliably. In this layer data begins to take shape and it is not uncommon to have some end-user application access data from this layer especially if they are time sensitive, as data becomes available here before it is transformed into the Access and Performance layer.

- Access layer. This layer is traditionally defined as a snowflake or star schema that describes a "flattened" or dimensional view of the data.

Layers in the Default Oracle Utilities Data Model Warehouse

Oracle Utilities Data Model warehouse environment also consists of three layers However, as indicated by the dotted line in Figure 2-1, "Layers of an Oracle Utilities Data Model Warehouse", in the Oracle Utilities Data Model the definitions of the foundation and access layers are combined in a single schema.

Figure 2-1 Layers of an Oracle Utilities Data Model Warehouse

The layers in the Oracle Utilities Data Model warehouse are:

- Staging layer. As in a "traditional" data warehouse environment, an Oracle Utilities Data Model warehouse environment can have a staging layer. Because the definition of this layer varies by customer, a definition of this area is not provided as part of Oracle Utilities Data Model.

- Foundation and Access layers. The physical objects for these layers are defined in a single schema, the oudm_sys schema:

- Foundation layer. The foundation layer of the Oracle Utilities Data Model is defined by base tables that present the data in 3NF (that is, tables that have the DWB_ prefix). This layer also includes reference, lookup, and control tables defined in the oudm_sys schema (that is, the tables that have the DWR_ , DWL_, DWC_ prefixes).

- Access layer. The access layer of Oracle Utilities Data Model is defined by derived and aggregate tables (defined with DWD_ and DWA_ prefixes), cubes (defined with a CB$ prefix), and views (that is, views defined with the DWV_ prefix), and cube views (defined with _VIEW suffix). These structures provide a summarized or "flattened" perspectives of the data in the foundation layer.
This layer also contains the results of the data mining models which are stored in derived (DWD_) tables. The access layer also includes the tables with prefixes as shown in Table 2-1.

See:
Oracle Utilities Data Model Reference for detailed information on the oudm_sys schema.

Customizing the Oracle Utilities Data Model Physical Model

The starting point for the Oracle Utilities Data Model physical data model is the 3NF logical data model. The physical data model mirrors the logical model as much as possible, (although some changes in the structure of the tables or columns may be necessary) and defines database objects (such as tables, cubes, and views).

To customize the default physical model of the Oracle Utilities Data Model take the following steps:

1. Answer the questions outlined in "Questions to Answer Before You Customize the Physical Model".

2. Familiarize yourself with the characteristics of the logical and physical model of Oracle Utilities Data Model as outlined in"Characteristics of the Default Physical Model" and presented in detail in Oracle Utilities Data Model Reference.

3. Modify the foundation level of your physical model of Oracle Utilities Data Model, as needed. See "Common Change Scenarios" for a discussion of when customization might be necessary.
When defining physical structures:

- Keep the foundation layer in 3NF form.

- Use the information presented in "General Recommendations When Designing Physical Structures" to guide you when designing the physical objects.

- Follow the conventions used when creating the default physical model of Oracle Utilities Data Model as outlined in "Conventions When Customizing the Physical Model".

Tip:
Package the changes you make to the physical data model as a patch to the oudm_sys schema.

4. Modify the access layer of your physical model of Oracle Utilities Data Model as discussed in Chapter 3, "Access Layer Customization".

5. Modify existing or create a new Intra-ETL packages to feed the changes you make in the access layer, as discussed in Chapter 4, "ETL Implementation and Customization".

Questions to Answer Before You Customize the Physical Model

When designing the physical model remember that the logical data model is not one-to-one with the physical data model. Consider the load, query, and maintenance requirements when you convert the logical data model into the physical layer. For example, answer the following questions before you design the physical data model:

- Do you need the physical data model to cover the full scope of the logical data model, or only part of the scope?
"Common Change Scenarios" provides an overview discussion of making physical data model changes when your business needs do not result in a logical model that is the same as the Oracle Utilities Data Model logical model.

- What is the result of the source data profile?

- What is the data load frequency for each table?

- How many large tables are there and which tables are these?

- How will the tables and columns be accessed? What are the common joins?

- What is your data backup strategy?

Conventions When Customizing the Physical Model

When developing the physical model for Oracle Utilities Data Model, the naming conventions outlined were followed. Continue to follow these conventions as you customize the physical model.

General Naming Conventions for Physical Objects

Follow these guidelines for naming physical objects that you define:

- When naming the physical objects follow the naming guidelines for naming objects within an Oracle Database schema. For example:

- Table and column names must start with a letter, can use only 30 alphanumeric characters or less, cannot contain spaces or some special characters such as "!" and cannot use reserved words.

- Table names must be unique within a schema that is shared with views and synonyms.

- Column names must be unique within a table.

- Although it is common to use abbreviations in the physical modeling stage, as much as possible, use names for the physical objects that correspond to the names of the entities in the logical model. Use consistent abbreviations to avoid programmer and user confusion.

- When naming columns, use short names if possible. Short column names reduce the time required for SQL command parsing.

- The oudm_sys schema delivered with Oracle Utilities Data Model uses the prefixes and suffixes shown Table 2-1 to identify object types.

Table 2-1 Default Physical Object Prefixes and Suffixes in Oracle Utilities Data Model

Prefix or Suffix

Used for Name of These Objects

_VIEW

A relational view of an OLAP cube, dimension, or hierarchy.

CCB_

Customized OLAP cubes.

CUBE

Created when OLAP cubes are built. Used to store logs and results.

DM$

Created when the mining models are trained. Used to store trained model and logs.

DR$

Created when the mining models are trained. Used to store trained model and logs.

DWA_

Aggregate tables which are materialized views.

DWB_

Base transaction data (3NF) tables.

DWC_

Control tables.

DWD_

Derived tables -- including data mining result tables.

DWL_

Lookup tables.

DWR_

Reference data tables.

DWV_

Relational view of time dimension

See:
Oracle Utilities Data Model Reference for detailed information about the objects in the default Oracle Utilities Data Model.

Domain Definition Standards

A domain is a set of values allowed for a column. The domain can be enforced by a foreign key, check constraints, or the application on top of the database. Define the standards for each domain across the model such as:

- Date and time type, such as 'YYYY-MM-DD'. For example, be aware that most date columns (abbreviation DT) in Oracle Utilities Data Model may contain the time, such as EVT_STRT_DT. There is no separate TIME column.

- Numeric value in different situations. For example, all columns of type COUNT are NUMBER(9,0) while all monetary-like columns (AMOUNT) are NUMBER(16,5).

- Character string length in different situations. For example, all Code columns are VARCHAR2(100), Name (NAME) and Description columns (DSCR) are respectively 500 and 1000 characters long (with some exceptions). Indicator columns (IND) are CHAR(1).

- Coded value definition such as key or description. For example, all "Key" columns are NUMBER(30).

Foundation Layer Customization

The first step in customizing the physical model of Oracle Utilities Data Model is customizing the foundation layer of the physical data model. Since, as mentioned in "Layers in the Default Oracle Utilities Data Model Warehouse", the foundation layer of the physical model mirrors the 3NF logical model of Oracle Utilities Data Model, you might choose to customize the foundation layer to reflect differences between your logical model needs and the default logical model of Oracle Utilities Data Model. Additionally, you might need to customize the physical objects in the foundation layer to improve performance (for example, you might choose to compress some foundation layer tables).

When making changes to the foundation layer, keep the following points in mind:

- When changing the foundation layer objects to reflect your logical model design, make as few changes as possible. "Common Change Scenarios" outlines the most common customization changes you will make in this regard.

- When defining new foundation layer objects or when redesigning existing foundation layer objects for improved performance, follow the "General Recommendations When Designing Physical Structures" and "Conventions When Customizing the Physical Model".

- Remember that changes to the foundation layer objects can also impact the access layer objects.

Note:
Approach any attempt to change the Oracle Utilities Data Model with caution. The foundation layer of the physical model of the Oracle Utilities Data Model has (at its core) a set of generic structures that allow it to be flexible and extensible. You may disable temporarily Foreign Keys and constraints if required but do not forget to set them back when the entities are back in use. Before making extensive additions, deletions, or changes, ensure that you understand the full range of capabilities of Oracle Utilities Data Model and that you cannot handle your requirements using the default objects in the foundation layer.
Before making changes, review Appendix C, "Product Assumptions" in Oracle Utilities Data Model Reference. For more information, see Oracle Utilities Data Model Reference.

Common Change Scenarios

There are several common change scenarios when customizing the foundation layer of the physical data model:

- Additions to Existing Structures
If you identify business areas or processes that are not supported in the default foundation layer of the physical data model of Oracle Utilities Data Model, add new tables and columns.

Carefully study the default foundation layer of the physical data model of Oracle Utilities Data Model (and the underlying logical data model) to avoid building redundant structures when making additions. If these additions add high value to your business value, communicate the additions back to the Oracle Utilities Data Model Development Team for possible inclusion in future releases of Oracle Utilities Data Model.

- Deletions of Existing Structures
If there are areas of the model that cannot be matched to any of the business requirements of your legacy systems, it is safer to keep these structures and not populate that part of the warehouse.

Deleting a table in the foundation layer of the physical data model can destroy relationships needed in other parts of the model or by applications based on the it. Some tables may not be needed during the initial implementation, but you may want to use these structures at a later time. If this is a possibility, keeping the structures now saves re-work later. If tables are deleted, perform a thorough analysis to identify all relationships originating from that entity.

- Changes to Existing Structures
In some situations some structures in the foundation layer of the physical data model of the Oracle Utilities Data Model may not exactly match the corresponding structures that you use.

Before implementing changes, identify the impact that the changes would have on the database design of Oracle Utilities Data Model. Also identify the impact on any applications based on the new design.

General Recommendations When Designing Physical Structures

The oudm_sys schema delivered with Oracle Utilities Data Model was designed and defined following best practices for data access and performance. Continue to use these practices when you add new physical objects. This section provides information about how decisions about the following physical design aspects were made to the default Oracle Utilities Data Model:

- Tablespaces in the Oracle Utilities Data Model

- Data Compression in the Oracle Utilities Data Model

- Tables for Supertype and Subtype Entities in Oracle Utilities Data Model

- Surrogate Keys in the Physical Model

- Integrity Constraints in Oracle Utilities Data Model

- Indexes and Partitioned Indexes in the Oracle Utilities Data Model

- Partitioned Tables in the Oracle Utilities Data Model

- Parallel Execution in the Oracle Utilities Data Model

Tablespaces in the Oracle Utilities Data Model

A tablespace consists of one or more data files, which are physical structures within the operating system you are using.

Recommendations: Defining Tablespaces

If possible, define tablespaces so that they represent logical business units.

Use ultra large data files for a significant improvement in very large Oracle Utilities Data Model warehouse.

Changing the Tablespace and Partitions Used by Tables

You can change the tablespace and partitions used by Oracle Utilities Data Model tables. What you do depends on whether the Oracle Utilities Data Model table has partitions:

- For tables that do not have partitions (that is, lookup tables and reference tables), you can change the existing tablespace for a table.
By default, Oracle Utilities Data Model defines the partitioned tables as interval partitioning, which means the partitions are created only when new data arrives.

Consequently, for Oracle Utilities Data Model tables that have partitions (that is, Base, Derived, and Aggregate tables), for the new interval partitions to be generated in new tablespaces rather than current ones, issue the following statements.

ALTER TABLE table_name MODIFY DEFAULT ATTRIBUTES
TABLESPACE new_tablespace_name;

When new data is inserted in the table specified by table_name, a new partition is automatically created in the tablespace specified by tablespace new_tablespace_name.

- For tables that have partitions (that is, base, derived, and aggregate tables), you can specify that new interval partitions be generated into new tablespaces.
For Oracle Utilities Data Model tables that do not have partitions (that is, lookup tables and reference tables), to change the existing tablespace for a table then issue the following statement.

ALTER TABLE table_name MOVE TABLESPACE new_tablespace_name;

Data Compression in the Oracle Utilities Data Model

A key decision that you must make is whether to compress your data. Using table compression reduces disk and memory usage, often resulting in better scale-up performance for read-only operations. Table compression can also speed up query execution by minimizing the number of round trips required to retrieve data from the disks. Compressing data however imposes a performance penalty on the load speed of the data.

Recommendations: Data Compression

In general, choose to compress the data. The overall performance gain typically outweighs the cost of compression.

If you decide to use compression, consider sorting your data before loading it to achieve the best possible compression rate. The easiest way to sort incoming data is to load it using an ORDER BY clause on either the CTAS or IAS statement ("Create Table As Select" or "Insert As Select" types of statements). Specify an ORDER BY a NOT NULL column (ideally non numeric) that has many distinct values (1,000 to 10,000).

See also:
"Types of Data Compression Available".

Types of Data Compression Available

Oracle Database offers the following types of compression:

- Basic or Standard Compression

- OLTP Compression

- Hybrid Columnar Compression

Basic or Standard Compression

With standard compression Oracle Database compresses data by eliminating duplicate values in a database block. Standard compression only works for direct path operations (CTAS or IAS). If the data is modified using any kind of conventional DML operation (for example updates), the data within that database block is uncompressed to make the modifications and is written back to disk uncompressed.

By using a compression algorithm specifically designed for relational data, Oracle Database can compress data effectively and in such a way that Oracle Database incurs virtually no performance penalty for SQL queries accessing compressed tables.

Oracle Utilities Data Model leverages the compress feature for all base, derived, and aggregate tables which reduces the amount of data being stored, reduces memory usage (more data per memory block), and increases query performance.

You can specify table compression by using the COMPRESS clause of the CREATE TABLE statement or you can enable compression for an existing table by using ALTER TABLE statement as shown:

alter table <tablename> move compress;

Example 2-1 Creating a Compressed Table for Oracle Utilities Data Model

To create a compressed table named CWB_ACCS_MTHD_PORT_HIST.

COMPRESS
Create table CWB_ACCS_MTHD_PORT_HIST
(NP_RQST_HDR_CD                 VARCHAR2(30)
,ACCS_MTHD_KEY                   NUMBER(30)  NOT NULL ENABLE
,EXTRNL_OPRTR_KEY                NUMBER(30)
…..
)
tablespace TBS_BASE
COMPRESS ;

OLTP Compression

OLTP compression is a component of the Advanced Compression option. With OLTP compression, just like standard compression, Oracle Database compresses data by eliminating duplicate values in a database block. But unlike standard compression OLTP compression allows data to remain compressed during all types of data manipulation operations, including conventional DML such as INSERT and UPDATE.

See:
Oracle Database Administrator's Guide for more information on OLTP table compression features.

Oracle by Example:
For more information on Oracle Advanced Compression, see the "Using Table Compression to Save Storage Costs" OBE tutorial.
To access the tutorial, open the Oracle Learning Library in your browser by following the instructions in "Oracle Technology Network"; and, then, search for the tutorial by name.

Hybrid Columnar Compression

is available with some storage formats and achieves its compression using a logical construct called the compression unit which is used to store a set of hybrid columnar-compressed rows. When data is loaded, a set of rows is pivoted into a columnar representation and compressed. After the column data for a set of rows has been compressed, it is fit into the compression unit. If conventional DML is issued against a table with Hybrid Columnar Compression, the necessary data is uncompressed to do the modification and then written back to disk using a block-level compression algorithm.

Tip:
If your data set is frequently modified using conventional DML, then the use of Hybrid Columnar Compression is not recommended; instead, the use of OLTP compression is recommended.

Hybrid Columnar Compression provides different levels of compression, focusing on query performance or compression ratio respectively. With Hybrid Columnar Compression optimized for query, fewer compression algorithms are applied to the data to achieve good compression with little to no performance impact. However, compression for archive tries to optimize the compression on disk, irrespective of its potential impact on the query performance.

See also:
The discussion on Hybrid Columnar Compression in Oracle Database Concepts.

Tables for Supertype and Subtype Entities in Oracle Utilities Data Model

A supertype is a generic entity type that has a relationship with one or more subtypes.

A subtype is a sub-grouping of the entities in an entity type that is meaningful to the organization and that shares common attributes or relationships distinct from other subgroups.

- Subtypes inherit all supertype attributes

- Subtypes have attributes that are different from other subtypes

Recommendations: Tables for Supertype and Subtype Entities

Create separate tables for the super type and all sub type entities for the following reasons:

- Data integrity enforced at database level. (using NOT NULL column constraints)

- Relationships can be accurately modeled and enforced including those which apply to only one subtype

- Physical model closely resembles the logical data model.

- It is easier to correlate the logical data model with the physical data model and support the logical data model enhancements and changes.

- Physical data model reflects true business rules (for example, if there are some attributes or relationships mandatory for only one subtype.)

Surrogate Keys in the Physical Model

The surrogate key method for primary key construction involves taking the natural key components from the source systems and mapping them through a process of assigning a unique key value to each unique combination of natural key components (including source system identifier). The resulting primary key value is completely non-intelligent and is typically a numeric data type for maximum performance and storage efficiency.

Advantages of Surrogate keys include:

- Ensure uniqueness: data distribution

- Independent of source systems

- Re-numbering

- Overlapping ranges

- Uses the numeric data type which is the most performant data type for primary keys and joins

Disadvantages of Surrogate keys:

- Have to allocate during ETL

- Complex and expensive re-processing and data quality correction

- Not used in queries – performance impact

- The operational business intelligence requires natural keys to join to operational systems

Integrity Constraints in Oracle Utilities Data Model

Integrity constraints are used to enforce business rules associated with your database and to prevent having invalid information in the tables. The most common types of constraints include:

- PRIMARY KEY constraints, this is usually defined on the surrogate key column to ensure uniqueness of the record identifiers. In general, it is recommended that you specify the ENFORCED ENABLED RELY mode.

- UNIQUE constraints, to ensure that a given column (or set of columns) is unique. For slowly changing dimensions, it is recommended that you add a unique constraint on the Business Key and the Effective From Date columns to allow tracking multiple versions (based on surrogate key) of the same Business Key record.

- NOT NULL constraints, to ensure that no null values are allowed. For query rewrite scenarios, it is recommended that you have an inline explicit NOT NULL constraint on the primary key column in addition to the primary key constraint.

- FOREIGN KEY constraints, to ensure that relation between tables are being honored by the data. Usually in data warehousing environments, the foreign key constraint is present in RELY DISABLE NOVALIDATE mode.

The Oracle Database uses constraints when optimizing SQL queries. Although constraints can be useful in many aspects of query optimization, constraints are particularly important for query rewrite of materialized views. Under some specific circumstances, constraints need space in the database. These constraints are in the form of the underlying unique index.

Unlike data in many relational database environments, data in a data warehouse is typically added or modified under controlled circumstances during the extraction, transformation, and loading (ETL) process, therefore, most foreign key columns in Oracle Utilities Data Model are nullable.

Indexes and Partitioned Indexes in the Oracle Utilities Data Model

Indexes are optional structures associated with tables or clusters. In addition to the classical B-tree indexes, bitmap indexes are very common in data warehousing environments

- Bitmap indexes are optimized index structures for set-oriented operations. Additionally, they are necessary for some optimized data access methods such as star transformations. Bitmap indexes are typically only a fraction of the size of the indexed data in the table.

- B-tree indexes are most effective for high-cardinality data: that is, for data with many possible values, such as customer_name or phone_number. However, fully indexing a large table with a traditional B-tree index can be prohibitively expensive in terms of disk space because the indexes can be several times larger than the data in the table. B-tree indexes can be stored specifically in a compressed manner to enable huge space savings, storing more keys in each index block, which also leads to less I/O and better performance.

Recommendations: Indexes and Partitioned Indexes

Make the majority of the indexes in your customized Oracle Utilities Data Model bitmap indexes.

Use B-tree indexes only for unique columns or other columns with very high cardinalities (that is, columns that are almost unique). Store the B-tree indexes in a compressed manner.

Partition the indexes. Indexes are just like tables in that you can partition them, although the partitioning strategy is not dependent upon the table structure. Partitioning indexes makes it easier to manage the data warehouse during refresh and improves query performance.

Typically, specify the index on a partitioned table as local. Bitmap indexes on partitioned tables must always be local. B-tree indexes on partitioned tables can be global or local. However, in a data warehouse environment, local indexes are more common than global indexes. Use global indexes only when there is a specific requirement which cannot be met by local indexes (for example, a unique index on a non-partitioning key, or a performance requirement).

See also:
"Partitioned Tables in the Oracle Utilities Data Model", "Choosing a Cube Partitioning Strategy for Oracle Utilities Data Model", and "Partitioning and Materialized Views".

Partitioned Tables in the Oracle Utilities Data Model

Partitioning allows a table, index or index-organized table to be subdivided into smaller pieces. Each piece of the database object is called a partition. Each partition has its own name, and may optionally have its own storage characteristics. From the perspective of a database administrator, a partitioned object has multiple pieces that can be managed either collectively or individually. This gives the administrator considerable flexibility in managing partitioned objects. However, from the perspective of the application, a partitioned table is identical to a nonpartitioned table. No modifications are necessary when accessing a partitioned table using SQL DML commands.

As discussed in the following topics, partitioning can provide tremendous benefits to a wide variety of applications by improving manageability, availability, and performance:

- Partitioning the Oracle Utilities Data Model for Manageability

- Partitioning the Oracle Utilities Data Model for Easier Data Access

- Partitioning the Oracle Utilities Data Model for Join Performance

Oracle by Example:
To understand the various partitioning techniques in Oracle Database, see the "Manipulating Partitions in Oracle Database 11g" OBE tutorial.
To access the tutorial, open the Oracle Learning Library in your browser by following the instructions in "Oracle Technology Network"; and, then, search for the tutorial by name.

See also:
Indexes and Partitioned Indexes in the Oracle Utilities Data Model, Choosing a Cube Partitioning Strategy for Oracle Utilities Data Model, and Partitioning and Materialized Views.

Partitioning the Oracle Utilities Data Model for Manageability

Range partitioning helps improve the manageability and availability of large volumes of data.

Consider the case where two year's worth of sales data or 100 terabytes (TB) is stored in a table. At the end of each day a new batch of data must be to loaded into the table and the oldest days worth of data must be removed. If the Sales table is range partitioned by day then the new data can be loaded using a partition exchange load. This is a sub-second operation that has little or no impact on end user queries.

Oracle Utilities Data Model uses Interval Partitioning as an extension of Range Partitioning, so that you provide just the first partition higher limit and interval to create the first partition and the following partitions are created automatically as and when data comes. The (hidden) assumption is that the data flow is more or less similar over the various intervals.

Partitioning the Oracle Utilities Data Model for Easier Data Access

Range partitioning also helps ensure that only the necessary data to answer a query is scanned. Assuming that the business users predominately accesses the sales data on a weekly basis (for example, total sales per week) then range partitioning this table by day ensures that the data is accessed in the most efficient manner, as only seven partitions must be scanned to answer the business users query instead of the entire table. The ability to avoid scanning irrelevant partitions is known as partition pruning.

Partitioning the Oracle Utilities Data Model for Join Performance

Sub-partitioning by hash is used predominately for performance reasons. Oracle Database uses a linear hashing algorithm to create sub-partitions.

A major performance benefit of hash partitioning is partition-wise joins. Partition-wise joins reduce query response time by minimizing the amount of data exchanged among parallel execution servers when joins execute in parallel. This significantly reduces response time and improves both CPU and memory resource usage. In a clustered data warehouse, this significantly reduces response times by limiting the data traffic over the interconnect (IPC), which is the key to achieving good scalability for massive join operations. Partition-wise joins can be full or partial, depending on the partitioning scheme of the tables to be joined.

Figure 2-2 shows how a full partition-wise join divides a join between two large tables into multiple smaller joins. Each smaller join, performs a joins on a pair of partitions, one for each of the tables being joined. For the optimizer to choose the full partition-wise join method, both tables must be equi-partitioned on their join keys. That is, they have to be partitioned on the same column with the same partitioning method. Parallel execution of a full partition-wise join is similar to its serial execution, except that instead of joining one partition pair at a time, multiple partition pairs are joined in parallel by multiple parallel query servers. The number of partitions joined in parallel is determined by the Degree of Parallelism (DOP).

Figure 2-2 Partitioning for Join Performance

Recommendations: Number of Hash Partitions

In order to ensure that the data gets evenly distributed among the hash partitions it is highly recommended that the number of hash partitions is a power of 2 (for example, 2, 4, 8, and so on). A good rule of thumb to follow when deciding the number of hash partitions a table should have is 2 X # of CPUs rounded to up to the nearest power of 2.

If your system has 12 CPUs, then 32 would be a good number of hash partitions. On a clustered system the same rules apply. If you have 3 nodes each with 4 CPUs, then 32 would still be a good number of hash partitions. However, ensure that each hash partition is at least 16 MB. Many small partitions do not have efficient scan rates with parallel query. Consequently, if using the number of CPUs makes the size of the hash partitions too small, use the number of Oracle RAC nodes in the environment (rounded to the nearest power of 2) instead.

Parallel Execution in the Oracle Utilities Data Model

Parallel Execution enables a database task to be parallelized or divided into smaller units of work, thus allowing multiple processes to work concurrently. By using parallelism, a terabyte of data can be scanned and processed in minutes or less, not hours or days.

Figure 2-3 illustrates the parallel execution of a full partition-wise join between two tables, Sales and Customers. Both tables have the same degree of parallelism and the same number of partitions. They are range partitioned on a date field and sub partitioned by hash on the cust_id field. As illustrated in the picture, each partition pair is read from the database and joined directly.

There is no data redistribution necessary, thus minimizing IPC communication, especially across nodes. Figure 2-3 shows the execution plan you would see for this join.

Figure 2-3 Parallel Execution of a Full Partition-Wise Join Between Two Tables

To ensure that you get optimal performance when executing a partition-wise join in parallel, specify a number for the partitions in each of the tables that is larger than the degree of parallelism used for the join. If there are more partitions than parallel servers, each parallel server is given one pair of partitions to join, when the parallel server completes that join, it requests another pair of partitions to join. This process repeats until all pairs have been processed. This method enables the load to be balanced dynamically (for example, 128 partitions with a degree of parallelism of 32).

What happens if only one table that you are joining is partitioned? In this case the optimizer could pick a partial partition-wise join. Unlike full partition-wise joins, partial partition-wise joins can be applied if only one table is partitioned on the join key. Hence, partial partition-wise joins are more common than full partition-wise joins. To execute a partial partition-wise join, Oracle Database dynamically repartitions the other table based on the partitioning strategy of the partitioned table.

After the other table is repartitioned, the execution is similar to a full partition-wise join. The redistribution operation involves exchanging rows between parallel execution servers. This operation leads to interconnect traffic in Oracle RAC environments, since data must be repartitioned across node boundaries.

Figure 2-4 illustrates a partial partition-wise join. It uses the same example as in Figure 2-3, except that the customer table is not partitioned. Before the join operation is executed, the rows from the customers table are dynamically redistributed on the join key.

Figure 2-4 Partial Partition-Wise Join

Enabling Parallel Execution for a Session

Parallel query is the most commonly used parallel execution feature in Oracle Database. Parallel execution can significantly reduce the elapsed time for large queries. To enable parallelization for an entire session, execute the following statement.

alter session enable parallel query;

Note:
It is usually suggested to set at session level rather than at the system level.

Enabling Parallel Execution of DML Operations

Data Manipulation Language (DML) operations such as INSERT, UPDATE, and DELETE can be parallelized by Oracle Database. Parallel execution can speed up large DML operations and is particularly advantageous in data warehousing environments. To enable parallelization of DML statements, execute the following statement.

alter session enable parallel dml;

When you issue a DML statement such as an INSERT, UPDATE, or DELETE, Oracle Database applies a set of rules to determine whether that statement can be parallelized. The rules vary depending on whether the statement is a DML INSERT statement, or a DML UPDATE or DELETE statement.

- The following rules apply when determining how to parallelize DML UPDATE and DELETE statements:

- Oracle Database can parallelize UPDATE and DELETE statements on partitioned tables, but only when multiple partitions are involved.

- You cannot parallelize UPDATE or DELETE operations on a nonpartitioned table or when such operations affect only a single partition.

- The following rules apply when determining how to parallelize DML INSERT statements:

- Standard INSERT statements using a VALUES clause cannot be parallelized.

- Oracle Database can parallelize only INSERT . . . SELECT . . . FROM statements.

Enabling Parallel Execution at the Table Level

The setting of parallelism for a table influences the optimizer. Consequently, when using parallel query, also enable parallelism at the table level by issuing the following statement.

alter table <table_name> parallel 32;

3 Access Layer Customization

This chapter provides information about customizing the access layer of Oracle Utilities Data Model. It includes the following topics:

- Introduction to Customizing the Access Layer of Oracle Utilities Data Model

- Derived Tables in the Oracle Utilities Data Model

- Aggregate Tables in the Oracle Utilities Data Model

- Dimensional Components in the Oracle Utilities Data Model

- Materialized Views in the Oracle Utilities Data Model

See also:
Chapter 2, "Physical Model Customization"

Introduction to Customizing the Access Layer of Oracle Utilities Data Model

The access layer of Oracle Utilities Data Model provides the calculated and summarized ("flattened") perspectives of the data needed by business intelligence tools. Access layer objects are populated using the data from the foundation layer 3NF objects.

The access layer objects in the oudm_sys schema include: derived and aggregate tables and OLAP cube views. This layer also contains data mining models. The results of these models are stored in derived tables. The models themselves are also defined in the oudm_sys schema.

When designing and customizing access layer objects:

- Follow the general guidelines for customizing physical objects given in "General Recommendations When Designing Physical Structures".

- Design access layer objects to support business intelligence reports and queries that your business end-users require. See Chapter 5, "Report and Query Customization".

The following topics provide specialized information about designing and customizing access layer objects:

- Derived Tables in the Oracle Utilities Data Model

- Aggregate Tables in the Oracle Utilities Data Model

- Dimensional Components in the Oracle Utilities Data Model

- Materialized Views in the Oracle Utilities Data Model

Derived Tables in the Oracle Utilities Data Model

Derived tables have a DWD_ prefix. Derived tables are tables where one of the following apply:

- Have values as the result of a non-aggregate calculation against the data in the foundation layer tables.

- Have some minimal level of aggregation, typically at the day level (for example, DWD_ACCT_DEBT_DAY).

- Summarize, at day or month level, all activities over the period of specific processes, split in various attributes (for example, DWD_ACCT_BAL_MO).

Some derived tables leverage, and hence are dependent on, other derived tables (for example DWD_CUST_DR_PROG_PROFILE).

Depending on the type of derived table you customize derived tables as follows:

- Tables that hold the results of a calculation such as DWD_ACCT_DEBT_DAY table that contains values that are daily DEBT_CNT (Debt Count) and DEBT_AMT (Debt Amount) statistics about debt. For information on customizing these tables, see "Creating New Derived Tables for Calculated Data".

- Result tables for the data mining models (for example, DWD_CUST_DR_PROG_PROFILE). For information on customizing data mining models, see "Customizing Oracle Utilities Data Model Data Mining Models".

See:
The Derived Tables topic in Oracle Utilities Data Model Reference for a list of all of the derived tables in the default Oracle Utilities Data Model. For a list of only those derived tables that are results tables for the data mining models, see the chapter on Data Mining Models in Oracle Utilities Data Model Reference.

Creating New Derived Tables for Calculated Data

If, during fit-gap analysis, you identified a need for calculated data that is not provided by the default derived tables, you can meet this need by defining new tables or, alternatively, by adding missing dimensions and measures to existing derived tables. When designing these tables, name the tables following the convention of using the CWD_ prefix (for Customized Warehouse Derived) or DWD_ (for Data Warehouse Derived). Make sure all the main dimensions are put first and have Foreign Keys to their corresponding reference or lookup tables. Attributes that add information only, avoiding costly joins, should not be part of the Primary Key of the derived table. Some dimensions that are part of a hierarchy do not necessarily need to be part of the Primary Key. All measures should be put afterward, grouped if possible by similar meaning.

See:
Oracle Utilities Data Model Reference for details on dimensions that are part of a hierarchy.

Customizing Oracle Utilities Data Model Data Mining Models

Some reference (DWR_) tables in the default oudm_sys schema are the results of data mining models defined in the default Oracle Utilities Data Model. Those models are defined in the default oudm_sys schema that also comes with Oracle Utilities Data Model. For Customer Savings and Customer Profile by DR program data mining model a separate derived table, DWD_CUST_DR_PROG_PROFILE, is created. Mining code includes a script to populate this table from the following source tables:

- DWD_MTR_RDNG_DAY

- DWR_CUST

- DWR_DEMAND_RESPN_PROG

- DWR_USG_PNT_GRP_DR_PROG_ASGN

- DWR_USG_PNT_GRP

- DWR_USG_PNT_GRP_ASGN

- DWR_USG_PNT

- DWR_ACCT

- DWR_CUST_ACCT_ASGN

Oracle Utilities Data Model data mining models get source data from views defined on the following derived tables (DWD_):

- DWD_CUST_DR_PROG_PROFILE

Data mining models prediction results and model rules are stored in derived tables (DWD_) and reference tables (DWR_). They are:

- DWD_CUST_DR_PROG_PROFILE

- DWR_CUST_SGMNT

- DWR_CUST_SGMNT_DTL

All mining source scripts are copied to $ORACLE_HOME/oudm/pdm/mining directory when Oracle Utilities Data Model is installed. Table 3-1 shows the Oracle Utilities Data Model mining scripts.

Table 3-1 Oracle Utilities Data Model Mining Scripts

Script Name

Description

oudm_mining_init.sql

Initializes mining environment and executes the other three mining scripts.

pkg_mining_etl.sql

Defines views, which have training/apply data, on source tables.

pkg_oudm_mining.sql

Core mining package that has a procedure for each model. Each procedure drops, creates mining model and scores mining model.

pkg_dwd_cust_dr_prog_profile.sql

A PL/SQL package to load data into mining source derived table DWD_CUST_DR_PROG_PROFILE.

When creating a customized Oracle Utilities Data Model warehouse, you can customize the data mining model in the following ways:

- Create a new model as discussed in "Creating a New Data Mining Model for Oracle Utilities Data Model".

- Modify an existing model as discussed in "Modifying Oracle Utilities Data Model Data Mining Models".

Creating a New Data Mining Model for Oracle Utilities Data Model

To create a data mining model:

1. Define the problem and identify input attributes. Also identify target attribute if the mining problem is supervised.

2. Check if the existing mining source views defined in pkg_mining_etl.sql script support the requirement of your problem. Modify the definition of views to support your requirement. Do not remove any columns from view definition unless you are sure that those columns do not make any sense.

3. If the existing mining source views do not support required fields, create a new table or view to support your requirements. Add the new table to pkg_mining_etl.sql PL/SQL package. Follow the naming conventions outlined in "Conventions When Customizing the Physical Model" and use a DWD_ prefix for results tables. Modify the intra-ETL programs to support your mining problem requirements.

4. For each mining problem that Oracle Data Mining supports, there is more than one algorithm. Create a setting table for your mining problem and follow the naming convention. The prefix for a setting table is "DM_". Add the definition of new setting table to oudm_mining_init.sql script.

5. Add a procedure for your mining problem to pkg_oudm_mining PL/SQL package. This procedure should create mining model and score the trained mining model on apply data. Compile the package. To create the mining model for your problem, invoke the newly added procedure. Make sure your new procedure works according to your expectations. Check user_mining_models data dictionary view for trained model. There are few more data dictionary views that give more information on the trained models. For more details, refer to Oracle Data Mining Concepts.

Modifying Oracle Utilities Data Model Data Mining Models

To customize Oracle Utilities Data Model mining models, take the following steps:

1. Change the definition of source views used as input to the mining model.

2. If required, change the definition of source derived table, DWD_CUST_DR_PROG_PROFILE. Do not remove any existing columns. Only add new columns with NULL enable.

3. Modify the PL/SQL package of DWD_CUST_DR_PROG_PROFILE table. Execute the package to load data into DWD_CUST_DR_PROG_PROFILE table.

4. Refresh mining views by executing following statement. You need to pass demand response program key:

SQL> exec PKG_MINING_ETL.refresh_mining_views(l_dr_prog_key);

5. Train the model again by calling Oracle Utilities Data Model mining package.

6. Ensure that the model reflects the new definition (for example, that a new column has been added).

Example 3-1 Adding a New Column to a Mining Model in Oracle Utilities Data Model

To add a new column to Customer Savings and Customer Profile by DR Program, take the following steps:

1. Add the new column to views that are used in the mining model.

- DWV_FRST_STEP_CUST_SGMNT_SRC

2. Train the model by issuing the following statement.

pkg_oudm_mining. crt_frst_step_cust_sgmnt_model(l_dr_prog_key);

3. Execute the following statement to query model details table and ensure the new column name is included in the query result:

SQL> SELECT attribute_name
FROM user_mining_model_attributes
WHERE model_name = 'OUDM_PROFILE_KMEANS_' || l_dr_prog_key;

See also:
"Refreshing the Data in an Oracle Utilities Data Model Warehouse".

Aggregate Tables in the Oracle Utilities Data Model

Aggregate tables are tables that aggregate or "roll up" the data to one level higher than a base or derived table (and other functions can also be in the aggregate tables such as average, count, min, max, and others). The aggregate tables in the default Oracle Utilities Data Model are actually materialized views and have a DWA_ prefix. These aggregate tables provide a view of the data similar to the view provided by a fact table in a snowflake schema.

The default Oracle Utilities Data Model defines several aggregate tables. For example, the DWA_END_DVC_EVT_DVC_MO table aggregates the values of the DWD_END_DVC_EVT_DVC_DAY table to the month level.

See:
The "Aggregate Tables" topic in Oracle Utilities Data Model Reference for a list of the aggregate tables in the default Oracle Utilities Data Model.

If, during fit-gap analysis, you identified a need for simple aggregated data that is not provided by the default aggregate tables, you can define new materialized views. When designing these tables, keep the following points in mind:

- Create a query for the materialized view that aggregates up only a single level. For example, if aggregating over time, then aggregate only from day to month.

Note:
When you must aggregate up many levels (for example in time, month, quarter, and year) or different hierarchies (for example, the fiscal and calendar hierarchies for a time dimension), do not define a DWA_ table; instead, define the aggregations by creating OLAP cubes.

See also:
"Materialized Views in the Oracle Utilities Data Model" and "Defining New Oracle OLAP Cubes for Oracle Utilities Data Model".

- Name the tables following the conventions outlined in "Conventions When Customizing the Physical Model" and use a DWA_ prefix.

Dimensional Components in the Oracle Utilities Data Model

There is often much discussion regarding the 'best' modeling approach to take for any given data warehouse with each style, classic 3NF and dimensional having their own strengths and weaknesses. It is likely that data warehouses must do more to embrace the benefits of each model type rather than rely on just one - this is the approach that was adopted in designing the Oracle Utilities Data Model. The foundation layer of the Oracle Utilities Data Model is a 3NF model. The default Oracle Utilities Data Model also provides a dimensional model of the data. This dimensional model of the data is a perspective that summarizes and aggregates data, rather than preserving detailed transaction information.

Familiarize yourself with dimensional modeling by reading the following topics before you begin to customize the dimensional model of the default Oracle Utilities Data Model:

- Characteristics of a Dimensional Model

- Characteristics of Relational Star and Snowflake Tables

- Characteristics of the OLAP Dimensional Model

- Characteristics of the OLAP Cubes in Oracle Utilities Data Model

- Defining New Oracle OLAP Cubes for Oracle Utilities Data Model

- Changing an Oracle OLAP Cube in Oracle Utilities Data Model

- Creating a Forecast Cube for Oracle Utilities Data Model

- Choosing a Cube Partitioning Strategy for Oracle Utilities Data Model

- Choosing a Cube Data Maintenance Method for Oracle Utilities Data Model

Characteristics of a Dimensional Model

The simplicity of a dimensional model is inherent because it defines objects that represent real-world business entities. Analysts know which business measures they are interested in examining, which dimensions and attributes make the data meaningful, and how the dimensions of their business are organized into levels and hierarchies.

In the simplest terms, a dimensional model identifies the following objects:

- Measures. Measures store quantifiable business data (such as sales, expenses, and inventory). Measures are also called "facts". Measures are organized by one or more dimensions and may be stored or calculated at query time:

- Stored Measures. Stored measures are loaded and stored at the leaf level. Commonly, there is also a percentage of summary data that is stored. Summary data that is not stored is dynamically aggregated when queried.

- Calculated Measures. Calculated measures are measures whose values are calculated dynamically at query time. Only the calculation rules are stored in the database. Common calculations include measures such as ratios, differences, totals and moving averages. Calculations do not require disk storage space, and they do not extend the processing time required for data maintenance.

- Dimensions. A dimension is a structure that categorizes data to enable users to answer business questions. Commonly used dimensions are Customers, Products, and Time. A dimension's structure is organized hierarchically based on parent-child relationships. These relationships enable:

- Navigation between levels.
Hierarchies on dimensions enable drilling down to lower levels or navigation to higher levels (rolling up). Drilling down on the Time dimension member 2012 typically navigates you to the quarters Q1 2012 through Q4 2012. In a calendar year hierarchy for 2012, drilling down on Q1 2012 would navigate you to the months, January 12 through March 12. These kinds of relationships make it easy for users to navigate through large volumes of multidimensional data.

- Aggregation from child values to parent values.
The parent represents the aggregation of its children. Data values at lower levels aggregate into data values at higher levels. Dimensions are structured hierarchically so that data at different levels of aggregation are manipulated efficiently for analysis and display.

- Allocation from parent values to child values.
The reverse of aggregation is allocation and is heavily used by planning budgeting, and similar applications. Here, the role of the hierarchy is to identify the children and descendants of particular dimension members of "top-down" allocation of budgets (among other uses).

- Grouping of members for calculations.
Share and index calculations take advantage of hierarchical relationships (for example, the percentage of total profit contributed by each product, or the percentage share of product revenue for a certain category, or costs as a percentage of the geographical region for a retail location).

A dimension object helps to organize and group dimensional information into hierarchies. This represents natural 1:n relationships between columns or column groups (the levels of a hierarchy) that cannot be represented with constraint conditions. Going up a level in the hierarchy is called rolling up the data and going down a level in the hierarchy is called drilling down the data.

There are two ways that you can implement a dimensional model:

- Relational tables in a star schema configuration. This traditional method of implementing a dimensional model is discussed in "Characteristics of Relational Star and Snowflake Tables".

- Oracle OLAP Cubes. The physical model provided with Oracle Utilities Data Model provides a dimensional perspective of the data using Oracle OLAP cubes. This dimensional model is discussed in "Characteristics of the OLAP Dimensional Model".

Characteristics of Relational Star and Snowflake Tables

In the case of relational tables, the dimensional model has historically been implemented as a star or snowflake schema. Dimension tables (which contain information about hierarchies, levels, and attributes) join to one or more fact tables. Fact tables are the large tables that store quantifiable business measurements (such as sales, expenses, and inventory) and typically have foreign keys to the dimension tables. Dimension tables, also known as lookup or reference tables, contain the relatively static or descriptive data in the data warehouse.

A star schema is a relational schema whose design represents a multidimensional data model. The star schema consists of one or more fact tables and one or more dimension tables that are related through foreign keys. This allows drill paths, hierarchy and query profile to be embedded in the data model itself rather than the data. This in part at least, is what makes navigation of the model so straightforward for end users. Star schemas usually have a large fact table surrounded by smaller dimension tables. Dimension tables do not change very much. Most of the information that the users need are in the fact tables. Therefore, star schemas have fewer table joins than do 3NF models.

A star schema is so called because the diagram resembles a star, with points radiating from a center. The center of the star consists of one or more fact tables and the points of the star are the dimension tables.

Figure 3-1 Star Schema Diagram

Snowflake schemas are slight variants of a simple star schema where the dimension tables are further normalized and broken down into multiple tables. The snowflake aspect only affects the dimensions and not the fact table and is therefore considered conceptually equivalent to star schemas. Snowflake dimensions are useful and indeed necessary when there are fact tables of differing granularity. A month-level derived or aggregate table (or materialized view) must be associated with a month level snowflake dimension table rather than the default (lower) Day level star dimension table.

See also:
"Declaring Relational Dimension Tables" and "Validating Relational Dimension Tables".

Declaring Relational Dimension Tables

When a relational table acts as a dimension to a fact table, it is recommended that you declare that table as a dimension (even though it is not necessary). Defined dimensions can yield significant performance benefits, and support the use of more complex types of rewrite.

To define and declare the structure of the dimension use the CREATE DIMENSION command. Use the LEVEL clause to identify the names of the dimension levels.

Validating Relational Dimension Tables

To improve the data quality of the dimension data in the data warehouse, it is recommended that you validate the declarative information about the relationships between the dimension members after any modification to the dimension data.

To perform this validation, use the VALIDATE_DIMENSION procedure of the DBMS_DIMENSION package. When the VALIDATE_DIMENSION procedure encounters any errors, the procedure places the errors into the DIMENSION_EXCEPTIONS table. To find the exceptions identified by the VALIDATE_DIMENSION procedure, query the DIMENSION_EXCEPTIONS table.

You can schedule a call to the VALIDATE_DIMENSION procedure as a post-process step to the regular Incremental Dimension load script. This can be done before the call to refresh the derived or aggregate tables of the data model through materialized view refresh, intra-ETL package calls.

Characteristics of the OLAP Dimensional Model

Oracle OLAP Cubes logically represent data similar to relational star tables, although the data is actually stored in multidimensional arrays. Like dimension tables, cube dimensions organize members into hierarchies, levels, and attributes. The cube stores the measure (fact) data. The dimensions form the edges of the cube.

Oracle OLAP is an OLAP server embedded in the Oracle Database. Oracle OLAP provides native multidimensional storage and speed-of-thought response times when analyzing data across multiple dimensions. The database provides rich support for analytics such as time series calculations, forecasting, advanced aggregation with additive and nonadditive operators, and allocation operations.

By integrating multidimensional objects and analytics into the database, Oracle Database provides the best of both worlds: the power of multidimensional analysis along with the reliability, availability, security, and scalability of the Oracle database.

Oracle OLAP is fully integrated into Oracle Database. At a technical level, this means:

- The OLAP engine runs within the kernel of Oracle Database.

- Dimensional objects are stored in Oracle Database in their native multidimensional format.

- Cubes and other dimensional objects are first class data objects represented in the Oracle data dictionary.

- Data security is administered in the standard way, by granting and revoking privileges to Oracle Database users and roles.

- OLAP cubes, dimensions, and hierarchies are exposed to applications as relational views. Consequently, applications can query OLAP objects using SQL as described in "Oracle OLAP Cube Views" and Chapter 5, "Report and Query Customization".

- Oracle OLAP cubes can be enhanced so that they are materialized views as described in "Cube Materialized Views".

See also:
Oracle OLAP User's Guide and"Characteristics of the OLAP Cubes in Oracle Utilities Data Model".

Benefits of Using Oracle OLAP

The benefits of using Oracle OLAP are significant; Oracle OLAP offers the power of simplicity and provides: One database, standard administration and security, standard interfaces and development tools.

The Oracle OLAP dimensional data model is highly structured. Structure implies rules that govern the relationships among the data and control how the data can be queried. Cubes are the physical implementation of the dimensional model, and thus are highly optimized for dimensional queries. The OLAP engine leverages this innate dimensionality in performing highly efficient cross-cube joins for inter-row calculations, outer joins for time series analysis, and indexing. Dimensions are pre-joined to the measures. The technology that underlies cubes is based on an indexed multidimensional array model, which provides direct cell access.

The OLAP engine manipulates dimensional objects in the same way that the SQL engine manipulates relational objects. However, because the OLAP engine is optimized to calculate analytic functions, and dimensional objects are optimized for analysis, analytic and row functions can be calculated much faster in OLAP than in SQL.

The dimensional model enables Oracle OLAP to support high-end business intelligence tools and applications such as OracleBI Discoverer Plus OLAP, OracleBI Spreadsheet Add-In, Oracle Business Intelligence Suite Enterprise Edition, BusinessObjects Enterprise, and Cognos ReportNet.

Oracle OLAP Dimensional Objects

Oracle OLAP dimensional objects include cubes, measures, dimensions, hierarchies, levels and attributes. The OLAP dimensional objects are described in detail in Oracle OLAP User's Guide. Figure 3-2 shows the general relationships among the objects.

Figure 3-2 Diagram of the OLAP Dimensional Model

Oracle OLAP Cube Views

When you define an OLAP cube, Oracle OLAP automatically generates a set of relational views on the cube and its dimensions and hierarchies

- Cube view. Each cube has a cube view that presents the data for all the measures and calculated measures in the cube. You can use a cube view like a fact table in a star or snowflake schema. However, the cube view contains all the summary data in addition to the detail level data. The default name of a cube view is cube_VIEW.

- Dimension and hierarchy views. Each dimension has one dimension view plus a hierarchy view for each hierarchy associated with the dimension. The default name for a dimension view is dimension_VIEW. For a hierarchy view, the default name is dimension_hierarchy_VIEW.

These views are related in the same way as fact and dimension tables in a star schema. Cube views serve the same function as fact tables, and hierarchy views and dimension views serve the same function as dimension tables. Typical queries join a cube view with either a hierarchy view or a dimension view.

SQL applications query these views to display the information-rich contents of these objects to analysts and decision makers. You can also create custom views that follow the structure expected by your applications, using the system-generated views like base tables.

See also:
The discussion on querying dimensional objects in Oracle OLAP User's Guide and Chapter 5, "Report and Query Customization".

Cube Materialized Views

Oracle OLAP cubes can be enhanced so that they are materialized views. A cube that has been enhanced in this way is called a cube materialized view and has a CB$ prefix. Cube materialized views can be incrementally refreshed through the Oracle Database materialized view subsystem, and they can serve as targets for transparent rewrite of queries against the source tables.

The OLAP dimensions associated with a cube materialized view are also defined with materialized view capabilities.

Necessary Cube Characteristics for Cube Materialized Views

A cube must conform to the following requirements, before it can be designated as a cube materialized view:

- All dimensions of the cube have at least one level and one level-based hierarchy. Ragged and skip-level hierarchies are not supported. The dimensions must be mapped.

- All dimensions of the cube use the same aggregation operator, which is either SUM, MIN, or MAX.

- The cube has one or more dimensions and one or more measures.

- The cube is fully defined and mapped. For example, if the cube has five measures, then all five are mapped to the source tables.

- The data type of the cube is NUMBER, VARCHAR2, NVARCHAR2, or DATE.

- The source detail tables support dimension and rely constraints. If they have not been defined, then use the Relational Schema Advisor to generate a script that defines them on the detail tables.

- The cube is compressed.

- The cube can be enriched with calculated measures, but it cannot support more advanced analytics in a cube script.

Adding Materialized View Capabilities

To add materialized view capabilities to an OLAP cube, take the following steps:

1. In the Analytic Workspace Manager, connect to the oudm_sys schema.

2. From the cube list, select the cube which you want to enable.

3. In the right pane, select the Materialized Views tab.

4. Select Enable Materialized View Refresh of the Cube. then click Apply.

Note:
You cannot enable the cube materialized view for a forecast cube.

Oracle by Example:
For more information on working with OLAP cubes, see the following OBE tutorials:

- "Querying OLAP 11g Cubes"

- "Using Oracle OLAP 11g With Oracle BI Enterprise Edition"

To access the tutorials, open the Oracle Learning Library in your browser by following the instructions in "Oracle Technology Network"; and, then, search for the tutorials by name.

See also:
Oracle OLAP User's Guide

Characteristics of the OLAP Cubes in Oracle Utilities Data Model

The default access layer of Oracle Utilities Data Model provides a dimensional perspective of the data using Oracle OLAP cubes.

There are OLAP cubes defined in the default oudm_sys schema. These cubes have the general characteristics described in "Characteristics of the OLAP Dimensional Model". Specifically, OLAP cubes in the Oracle Utilities Data Model have the following characteristics:

- All of the default OLAP cubes are loaded with data from DWA_ tables that are materialized views.

- The cubes were defined and built using the Analytical Workspace Manager (AWM) client tool.

- A relational view (with a _VIEW suffix) is defined over each of the OLAP cubes.

- All of the OLAP cubes in the Oracle Utilities Data Model are cube materialized views (that is, CB$ objects).

Note:
Immediately after installation, all materialized views underlying the OLAP cubes are disabled by default.

For information on the using OLAP cubes in your customized version of Oracle Utilities Data Model, see Oracle OLAP User's Guide and the following topics:

- Defining New Oracle OLAP Cubes for Oracle Utilities Data Model

- Changing an Oracle OLAP Cube in Oracle Utilities Data Model

- Creating a Forecast Cube for Oracle Utilities Data Model

- Choosing a Cube Partitioning Strategy for Oracle Utilities Data Model

- Choosing a Cube Data Maintenance Method for Oracle Utilities Data Model

Defining New Oracle OLAP Cubes for Oracle Utilities Data Model

You can add new OLAP cubes to the oudm_sys schema. For consistency's sake, design and define these new cubes as described in Characteristics of the OLAP Cubes in Oracle Utilities Data Model.

Take the following steps to define new cubes:

1. Ensure that there is an aggregate table (DWA_) to use as the "lowest leaf" data for the cube. See "Aggregate Tables in the Oracle Utilities Data Model" for information on creating new tables.

2. Use the AWM to define new Cubes for a customized version of Oracle Utilities Data Model. Follow the instructions given for creating cubes and dimensions in Oracle OLAP User's Guide.
Use the information provided in "Characteristics of the OLAP Dimensional Model". and the Oracle OLAP User's Guide to guide you when you design and define new OLAP cubes. Also, if you are familiar with a relational star schema design as outlined in "Characteristics of Relational Star and Snowflake Tables", then you can use this understanding to help you design an OLAP Cube:

- Fact tables correspond to cubes.

- Data columns in the fact tables correspond to measures.

- Foreign key constraints in the fact tables identify the dimension tables.

- Dimension tables identify the dimensions.

- Primary keys in the dimension tables identify the base-level dimension members.

- Parent columns in the dimension tables identify the higher level dimension members.

- Columns in the dimension tables containing descriptions and characteristics of the dimension members identify the attributes.

You can also get insights into the dimensional model by looking at the sample reports included with Oracle Utilities Data Model.

See:
Oracle Utilities Data Model Installation Guide for more information on installing the sample reports and deploying the Oracle Utilities Data Model RPD and webcat on the Business Intelligence Suite Enterprise Edition instance.

Tip:
While investigating your source data, you may decide to create relational views that more closely match the dimensional model that you plan to create.

3. Add materialized view capabilities to the OLAP cubes.

See also:
Oracle OLAP User's Guide, "Defining New Oracle OLAP Cubes for Oracle Utilities Data Model", and the sample reports in Oracle Utilities Data Model Reference.

Oracle by Example:
For more information on creating OLAP cubes, see the "Building OLAP 11g Cubes" OBE tutorial.
To access the tutorial, open the Oracle Learning Library in your browser by following the instructions in "Oracle Technology Network"; and, then, search for the tutorial by name.

Changing an Oracle OLAP Cube in Oracle Utilities Data Model

Common customizations to Oracle Utilities Data Model cubes are changing the dimensions or the measures of the cube.

Since all Oracle Utilities Data Model cubes load data from tables with the DWA_ prefix, to change the measures or dimensions of one cube, you must take the following steps:

1. Use the information in Oracle Utilities Data Model Reference, to identify the DWA_ table from which the OLAP cube is populated.

2. Change the structure of the DWA_ table identified in Step 1.

3. Change the OLAP cube and cube materialized views to reflect the new structure.

Creating a Forecast Cube for Oracle Utilities Data Model

To create a forecast cube for Oracle Utilities Data Model:

1. Create a cube to contain the results of the forecast as described in "Defining New Oracle OLAP Cubes for Oracle Utilities Data Model".

Note:
You cannot enable materialized views for an Oracle Utilities Data Model forecast cube.

2. Write an OLAP DML forecasting context program as described in Oracle OLAP DML Reference.

Choosing a Cube Partitioning Strategy for Oracle Utilities Data Model

Partitioning is a method of physically storing the contents of a cube. It improves the performance of large cubes in the following ways:

- Improves scalability by keeping data structures small. Each partition functions like a smaller measure.

- Keeps the working set of data smaller both for queries and maintenance, since the relevant data is stored together.

- Enables parallel aggregation during data maintenance. Each partition can be aggregated by a separate process.

- Simplifies removal of old data from storage. Old partitions can be dropped, and new partitions can be added.

The number of partitions affects the database resources that can be allocated to loading and aggregating the data in a cube. Partitions can be aggregated simultaneously when sufficient resources have been allocated.

The Cube Partitioning Advisor analyzes the source tables and develops a partitioning strategy. You can accept the recommendations of the Cube Partitioning Advisor, or you can make your own decisions about partitioning.

If your partitioning strategy is driven primarily by life-cycle management considerations, then you should partition the cube on the Time dimension. Old time periods can then be dropped as a unit, and new time periods added as a new partition. The Cube Partitioning Advisor has a Time option, which recommends a hierarchy and a level in the Time dimension for partitioning.

The level on which to partition a cube is determined based on a trade off between load performance and query performance.

Typically, you do not want to partition on too low a level (for example, on the DAY level of a TIME dimension) because if you do then too many partitions must be defined at load time which slows down an initial or historical load. Also, a large number of partitions can result in unusually long Analytic Workspace attach times and slows down the Time Series-based calculations. Also, a Quarterly Cumulative measure (Quarter to Date Measure) needs to access 90 or 91 partitions to calculate a value for one Customer and Organization. All dimension members above the partition level of partition dimension (including those belonging to nondefault hierarchies) would be present in a single default template. Day level partitioning makes this very heavy since all higher level members are stored in default template. However, the advantage of partitioning DAY if the OLAP Cube load frequency is daily then there you must only load from a new partition in fact table into a single partition in the OLAP cube every day. This greatly improves the load performance since percentage-based refresh can be enabled if the cube is materialized-view enabled and has materialized-view logs.

Recommendations: Cube Partitioning Strategy

Usually a good compromise between the differing load and query performance requirements is to use an intermediate level like MONTH as the partition level. Time series calculations within a month (week to date, month to date, and so on) are fast and higher level calculations like year to date needs to refer to 12 partitions at most. Also this way the monthly partition is defined and created only one time (that is during the initial load on first of each month) and is then reused for each subsequent load that month. The aggregation process may be triggered off at the month level (instead of specific day level) and some redundant aggregations (of previously loaded dates of current month) may occur each time but it should result in satisfactory load and query performance.

See also:
"The discussion on choosing a partition strategy in Oracle OLAP User's Guide, "Indexes and Partitioned Indexes in the Oracle Utilities Data Model", and "Partitioning and Materialized Views".

Choosing a Cube Data Maintenance Method for Oracle Utilities Data Model

While developing a dimensional model of your data, it is a good idea to map and load each object immediately after you create it so that you can immediately detect and correct any errors that you made to the object definition or the mapping.

However, in a production environment, you want to perform routine maintenance as quickly and easily as possible. For this stage, you can choose among data maintenance methods. You can refresh all cubes using the Maintenance Wizard. This wizard enables you to refresh a cube immediately, or submit the refresh as a job to the Oracle job queue, or generate a PL/SQL script. You can run the script manually or using a scheduling utility, such as Oracle Enterprise Manager Scheduler or the DBMS_SCHEDULER PL/SQL package. The generated script calls the BUILD procedure of the DBMS_CUBE PL/SQL package. You can modify this script or develop one from the start using this package.

The data for a partitioned cube is loaded and aggregated in parallel when multiple processes have been allocated to the build. You are able to see this in the build log.

In addition, each cube can support these data maintenance methods:

- Custom cube scripts

- Cube materialized views

If you are defining cubes to replace existing materialized views, then you use the materialized views as an integral part of data maintenance. Note, however, that materialized view capabilities restrict the types of analytics that can be performed by a custom cube script.

See also:
Oracle OLAP User's Guide and "Types of Materialized Views and Refresh Options"

Oracle by Example:
See the following OBE tutorial for an example of how Oracle uses cube materialized views for transparent access to a relational star schema.:

- "Querying OLAP 11g Cubes"

To access the tutorial, open the Oracle Learning Library in your browser by following the instructions in "Oracle Technology Network"; and, then, search for the tutorial by name.

Materialized Views in the Oracle Utilities Data Model

Materialized views are query results that have been stored or "materialized" in advance as schema objects. From a physical design point of view, materialized views resemble tables or partitioned tables and behave like indexes in that they are used transparently and can improve performance.

In the past, organizations using summaries spent a significant amount of time and effort creating summaries manually, identifying which summaries to create, indexing the summaries, updating them, and advising their users on which ones to use. With the advent of materialized views, a database administrator creates one or more materialized views, which are the equivalent of a summary. Thus, the workload of the database administrator is eased and the user no longer needed to be aware of the summaries that had been defined. Instead, the end user queries the tables and views at the detail data level. The query rewrite mechanism in the Oracle server automatically rewrites the SQL query to use the summary tables and reduces response time for returning results from the query.

Materialized views improve query performance by precalculating expensive join and aggregation operations on the database before executing and storing the results in the database. The query optimizer automatically recognizes when an existing materialized view can and should be used to satisfy a request.

The default Oracle Utilities Data Model defines many materialized views. In the default oudm_sys schema, you can identify these materialized views by looking at objects with the prefixes listed in the following table.

Prefix

Description

DWA_

Aggregate tables which are materialized views.
See: Aggregate tables in Oracle Utilities Data Model Reference for a list of these objects in the default data model.

"Aggregate Tables in the Oracle Utilities Data Model" for more information on customizing these objects,.

CB$

An OLAP cube enhanced with materialized view capabilities.
See: OLAP cube materialized views in Oracle Utilities Data Model Reference for a list of these objects in the default data model.

"Characteristics of the OLAP Cubes in Oracle Utilities Data Model" for information on OLAP cubes.

Note: Do not report or query against this object. Instead access the relational view of an OLAP cube (that is, the object with the _VIEW suffix).

The following topics provide more information on using and creating materialized views in your customized Oracle Utilities Data Model:

- Types of Materialized Views and Refresh Options

- Choosing Indexes for Materialized Views

- Partitioning and Materialized Views

- Compressing Materialized Views

Types of Materialized Views and Refresh Options

Refresh option vary by the type of materialized view:

- Refresh Options for Materialized Views with Aggregates

- Refresh Options for Materialized Views Containing Only Joins

- Refresh Options for Nested Materialized Views

See:
Oracle OLAP User's Guide for a discussion of creating materialized views of Oracle OLAP cubes.

Refresh Options for Materialized Views with Aggregates

In data warehouses, materialized views normally contain aggregates. The DWA_ tables in the default Oracle Utilities Data Model are this type of materialized view.

For a materialized view with aggregates, for fast refresh to be possible:

- The SELECT list must contain all of the GROUP BY columns (if present).

- There must be a COUNT(*) and a COUNT(column) on any aggregated columns.

- Materialized view logs must be present on all tables referenced in the query that defines the materialized view. The valid aggregate functions are: SUM, COUNT(x), COUNT(*), AVG, VARIANCE, STDDEV, MIN, and MAX, and the expression to be aggregated can be any SQL value expression.

Fast refresh for a materialized view containing joins and aggregates is possible after any type of DML to the base tables (direct load or conventional INSERT, UPDATE, or DELETE).

You can define that the materialized view be refreshed ON COMMIT or ON DEMAND. A REFRESH ON COMMIT materialized view is automatically refreshed when a transaction that does DML to a materialized view's detail tables commits.

When you specify REFRESH ON COMMIT, the table commit can take more time than if you have not. This is because the refresh operation is performed as part of the commit process. Therefore, this method may not be suitable if many users are concurrently changing the tables upon which the materialized view is based.

Refresh Options for Materialized Views Containing Only Joins

Some materialized views contain only joins and no aggregates (for example, when a materialized view is created that joins the sales table to the times and customers tables). The advantage of creating this type of materialized view is that expensive joins are precalculated.

Fast refresh for a materialized view containing only joins is possible after any type of DML to the base tables (direct-path or conventional INSERT, UPDATE, or DELETE).

A materialized view containing only joins can be defined to be refreshed ON COMMIT or ON DEMAND. If it is ON COMMIT, the refresh is performed at commit time of the transaction that does DML on the materialized view's detail table.

If you specify REFRESH FAST, Oracle Database performs further verification of the query definition to ensure that fast refresh can be performed if any of the detail tables change. These additional checks are:

- A materialized view log must be present for each detail table unless the table supports partition change tracking. Also, when a materialized view log is required, the ROWID column must be present in each materialized view log.

- The rowids of all the detail tables must appear in the SELECT list of the materialized view query definition.

If some of these restrictions are not met, you can create the materialized view as REFRESH FORCE to take advantage of fast refresh when it is possible. If one table does not meet all of the criteria, but the other tables do the materialized view is still fast refreshable with respect to the other tables for which all the criteria are met.

To achieve an optimally efficient refresh:

- Ensure that the defining query does not use an outer join that behaves like an inner join. If the defining query contains such a join, consider rewriting the defining query to contain an inner join.

- If the materialized view contains only joins, the ROWID columns for each table (and each instance of a table that occurs multiple times in the FROM list) must be present in the SELECT list of the materialized view.

- If the materialized view has remote tables in the FROM clause, all tables in the FROM clause must be located on that same site. Further, ON COMMIT refresh is not supported for materialized view with remote tables. Except for SCN-based materialized view logs, materialized view logs must be present on the remote site for each detail table of the materialized view and ROWID columns must be present in the SELECT list of the materialized view.

Refresh Options for Nested Materialized Views

A nested materialized view is a materialized view whose definition is based on another materialized view. A nested materialized view can reference other relations in the database in addition to referencing materialized views.

In a data warehouse, you typically create many aggregate views on a single join (for example, rollups along different dimensions). Incrementally maintaining these distinct materialized aggregate views can take a long time, because the underlying join has to be performed many times.

Using nested materialized views, you can create multiple single-table materialized views based on a joins-only materialized view and the join is performed just one time. In addition, optimizations can be performed for this class of single-table aggregate materialized view and thus refresh is very efficient.

Some types of nested materialized views cannot be fast refreshed. Use EXPLAIN_MVIEW to identify those types of materialized views.

You can refresh a tree of nested materialized views in the appropriate dependency order by specifying the nested =TRUE parameter with the DBMS_MVIEW.REFRESH parameter.

Choosing Indexes for Materialized Views

The two most common operations on a materialized view are query execution and fast refresh, and each operation has different performance requirements:

- Query execution might need to access any subset of the materialized view key columns, and might need to join and aggregate over a subset of those columns. Consequently, for best performance, create a single-column bitmap index on each materialized view key column.

- In the case of materialized views containing only joins using fast refresh, create indexes on the columns that contain the rowids to improve the performance of the refresh operation.

- If a materialized view using aggregates is fast refreshable, then an index appropriate for the fast refresh procedure is created unless USING NO INDEX is specified in the CREATE MATERIALIZED VIEW statement.

See also:
"Indexes and Partitioned Indexes in the Oracle Utilities Data Model"

Partitioning and Materialized Views

Because of the large volume of data held in a data warehouse, partitioning is an extremely useful option when designing a database. Partitioning the fact tables improves scalability, simplifies system administration, and makes it possible to define local indexes that can be efficiently rebuilt. Partitioning the fact tables also improves the opportunity of fast refreshing the materialized view because this may enable partition change tracking refresh on the materialized view.

Partitioning a materialized view has the same benefits as partitioning fact tables. When a materialized view is partitioned a refresh procedure can use parallel DML in more scenarios and partition change tracking-based refresh can use truncate partition to efficiently maintain the materialized view.

See also:
Oracle Database VLDB and Partitioning Guide, "Partitioned Tables in the Oracle Utilities Data Model", "Indexes and Partitioned Indexes in the Oracle Utilities Data Model", and "Choosing a Cube Partitioning Strategy for Oracle Utilities Data Model".

Using Partition Change Tracking

It is possible and advantageous to track freshness to a finer grain than the entire materialized view. The ability to identify which rows in a materialized view are affected by a certain detail table partition, is known as partition change tracking. When one or more of the detail tables are partitioned, it may be possible to identify the specific rows in the materialized view that correspond to a modified detail partition(s). those rows become stale when a partition is modified while all other rows remain fresh.

You can use partition change tracking to identify which materialized view rows correspond to a particular partition. Partition change tracking is also used to support fast refresh after partition maintenance operations on detail tables. For instance, if a detail table partition is truncated or dropped, the affected rows in the materialized view are identified and deleted. Identifying which materialized view rows are fresh or stale, rather than considering the entire materialized view as stale, allows query rewrite to use those rows that refresh while in QUERY_REWRITE_INTEGRITY = ENFORCED or TRUSTED modes.

Several views, such as DBA_MVIEW_DETAIL_PARTITION, detail which partitions are stale or fresh. Oracle does not rewrite against partial stale materialized views if partition change tracking on the changed table is enabled by the presence of join dependent expression in the materialized view.

To support partition change tracking, a materialized view must satisfy the following requirements:

- At least one detail table referenced by the materialized view must be partitioned.

- Partitioned tables must use either range, list or composite partitioning.

- The top level partition key must consist of only a single column.

- The materialized view must contain either the partition key column or a partition marker or ROWID or join dependent expression of the detail table.

- If you use a GROUP BY clause, the partition key column or the partition marker or ROWID or join dependent expression must be present in the GROUP BY clause.

- If you use an analytic window function or the MODEL clause, the partition key column or the partition marker or ROWID or join dependent expression must be present in their respective PARTITION BY subclauses.

- Data modifications can only occur on the partitioned table. If partition change tracking refresh is being done for a table which has join dependent expression in the materialized view, then data modifications should not have occurred in any of the join dependent tables.

- The COMPATIBILITY initialization parameter must be a minimum of 9.0.0.0.0.

- Partition change tracking is not supported for a materialized view that refers to views, remote tables, or outer joins.

Compressing Materialized Views

Using data compression for a materialized view brings you a additional dramatic performance improvement.

Consider data compression when using highly redundant data, such as tables with many foreign keys. In particular, likely candidates are materialized views created with the ROLLUP clause.

See also:
"Data Compression in the Oracle Utilities Data Model", and "Aggregate Tables in the Oracle Utilities Data Model".

4 ETL Implementation and Customization

This chapter discusses the ETL (extraction, transformation and loading) programs you use to populate an Oracle Utilities Data Model warehouse. It includes the following topics:

- The Role of ETL in the Oracle Utilities Data Model

- ETL for the Foundation Layer of an Oracle Utilities Data Model Warehouse

- Customizing Intra-ETL for Oracle Utilities Data Model

- Performing an Initial Load of an Oracle Utilities Data Model Warehouse

- Refreshing the Data in an Oracle Utilities Data Model Warehouse

- Managing Errors During Oracle Utilities Data Model Intra-ETL Execution

The Role of ETL in the Oracle Utilities Data Model

Figure 2-1, "Layers of an Oracle Utilities Data Model Warehouse" illustrates the three layers in Oracle Utilities Data Model warehouse environment: the optional staging layer, the foundation layer, and the access layer. As illustrated by Figure 4-1, you use two types of ETL (extraction, transformation and loading) to populate these layers:

- Source-ETL. ETL that populates the staging layer (if any) and the foundation layer (that is, the base, reference, and lookup tables) with data from the operational system is known as source ETL.
Oracle Utilities Data Model does not include source-ETL scripts. You must create source-ETL yourself using your understanding of your operational and other source systems and your customized Oracle Utilities Data Model. See"ETL for the Foundation Layer of an Oracle Utilities Data Model Warehouse" for more information on creating source-ETL.

- Intra-ETL. ETL that populates the access layer (that is, the derived tables, aggregate tables, materialized views, OLAP cubes, and data mining models) using the data in the foundation layer is known as intra-ETL.
Oracle Utilities Data Model does include intra-ETL. You can modify the default intra-ETL to populate a customized access layer from a customized foundation layer. See "Customizing Intra-ETL for Oracle Utilities Data Model" for more information on the intra-ETL.

Figure 4-1 ETL Flow Diagram

ETL for the Foundation Layer of an Oracle Utilities Data Model Warehouse

ETL that populates the foundation layer of an Oracle Utilities Data Model warehouse (that is, the base, reference, and lookup tables) with data from an operational system is known as source-ETL.

You can populate the foundation layer of an Oracle Utilities Data Model warehouse in the following ways:

- Write your own source-ETL scripts using Oracle Data Integrator or another ETL tool and then use those scripts to populate the foundation layer. For more information, see"Writing Your Own Source-ETL".

Writing Your Own Source-ETL

Using Oracle Utilities Data Model you must write your own source-ETL scripts using Oracle Data Integrator or another ETL tool or mapping tool.

The following topics provide general information about writing source-ETL:

- Source-ETL Design Considerations

- ETL Architecture for Oracle Utilities Data Model Source-ETL

- Creating a Source to Target Mapping Document for the Source-ETL

- Designing a Plan for Rectifying Source-ETL Data Quality Problems

- Designing Source-ETL Workflow and Jobs Control

- Designing Source-ETL Exception Handling

- Writing Source-ETL that Loads Efficiently

See Also:
Oracle® Fusion Middleware Developer's Guide for Oracle Data Integrator

Source-ETL Design Considerations

Keep the following points in mind when designing and writing source-ETL for Oracle Utilities Data Model:

- You can populate the calendar data by using the calendar population scripts provided with Oracle Utilities Data Model and described in Oracle Utilities Data Model Reference.

- Populate the tables in the following order:

1. Lookup tables

2. Reference tables

3. Base tables

- Analyze the tables in one category before loading the tables in the next category (for example, analyze the reference tables before loading the lookup tables). Additionally, you must analyze all of the tables loaded by the source-ETL process before executing the intra-ETL processes).

See:
The topic about analyzing tables, indexes, and clusters in Oracle Database Administrator's Guide.

ETL Architecture for Oracle Utilities Data Model Source-ETL

ETL (or EL-T, that is, Extract, Load and Transform) first extracts data from the original sources, assures the quality of the data, cleans the data, and makes the data consistent across the original sources. ETL then populates the physical objects with the "clean" data so that query tools, report writers, dashboards and so on can access the data.

The fundamental services upon which data acquisition is constructed are as follows:

- Data sourcing

- Data movement

- Data transformation

- Data loading

From a logical architecture perspective, there are many different ways to configure these building blocks for delivering data acquisition services. The major architectural styles available that cover a range of options to be targeted within a data warehousing architecture include:

- Batch Extract, Transform, and Load and Batch Extract, Load, Transform, Load
Batch Extract, Transform and Load (ETL) and Batch Extract, Load, Transform, Load (ELTL) are the traditional architecture's in a data warehouse implementation. The difference between them is where the transformation proceed in or out of the database.

- Batch Hybrid Extract, Transform, Load, Transform, Load
Batch Hybrid Extract, Transform, Load, Transform, Load (ETLTL) is a hybrid strategy. This strategy provides the most flexibility to remove hand coding approaches to transformation design, apply a metadata-driven approach, and still be able to leverage the data processing capabilities of the enterprise warehouse. In this targeted design, the transformation processing is first performed outside the warehouse as a pre-processing step before loading the staging tables, and then further transformation processing is performed within the data warehouse before the final load into the target tables.

- Real-time Extract, Transform, Load
Real-time Extract, Transform, Load (rETL) is appropriate when service levels for data freshness demand more up-to-date information in the data warehousing environment. In this approach, the OLTP system must actively publish events of interest so that the rETL processes can extract them from a message bus (queue) on a timely basis. A message-based paradigm is used with publish and subscribe message bus structures or point-to-point messaging with reliable queues. In such cases, the staging area can be used as a real-time Operational Data Store, at least for the source concerned, and aggregation could run directly from the Operational Data Store (operational system) to the Access layer, or to the presentation layer in specific cases.

When designing source-ETL for Oracle Utilities Data Model, use the architecture that best meets your business needs.

Creating a Source to Target Mapping Document for the Source-ETL

Before you begin building your extract systems, create a logical data interface document that maps the relationship between original source fields and target destination fields in the tables. This document ties the very beginning of the ETL system to the very end.

Columns in the data mapping document are sometimes combined. For example, the source database, table name, and column name could be combined into a single target column. The information within the concatenated column would be delimited with a period. Regardless of the format, the content of the logical data mapping document has been proven to be the critical element required to sufficiently plan ETL processes.

Designing a Plan for Rectifying Source-ETL Data Quality Problems

Data cleaning consists of all the steps required to clean and validate the data feeding a table and to apply known business rules to make the data consistent. The perspectives of the cleaning and conforming steps are less about the upside potential of the data and more about containment and control.

There are several potential data quality issues, related to each other, that the staging area needs to handle:

- Data Validity: Is the data content and type sufficient to be usable, and as expected (and "profile" in case one uses this advanced option)?

- Data Accuracy: correct addresses, correct with respect some "true" standard (or as such defined).

- Data Completeness: is all the required data there? What to do when data is missing? What represents the minimum set of required data?

- Data Consistency: that is, consistency of the data between the various sources and what rules one applies for inconsistencies.

- Data Latency: A sub-part of data consistency, but treated separately because of its importance: when does data arrive, over which period and in which one can we combine, which one not?

- Data Reasoning: This is more at reporting level but can be applied at the staging level: Does the data I see make sense from a business perspective? Can I really combine the data as an end-user would expect?

As a consequence, a multi-layer staging is generally required or expected.

If there are data quality problems, then build a plan, in agreement with IT and business users, for how to rectify these problems.

Answer the following questions:

- Is data missing?

- Is the data wrong or inconsistent?

- Should the problem be fixed in the source systems?

- Set up the data quality reporting and action program and people responsibility.

Set up the following processes and programs:

- Set up a data quality measurement process.

- Set up the data quality reporting and action program and people responsibility.

Designing Source-ETL Workflow and Jobs Control

All data movement among ETL processes are composed of jobs. An ETL workflow executes these jobs in the proper sequence and with the necessary dependencies. General ETL tools, such as Oracle Warehouse Builder, support this kind of workflow, job design, and execution control.

Tips for designing ETL jobs and workflow:

- Use common structure across all jobs (source system to transformer to target data warehouse).

- Have a one-to-one mapping from source to target.

- Define one job per Source table.

- Apply generic job structure and template jobs to allow for rapid development and consistency.

- Use an optimized job design to leverage Oracle load performance based on data volumes.

- Design parameterized job to allow for greater control over job performance and behavior.

- Maximize Jobs parallelism execution.

Designing Source-ETL Exception Handling

Your ETL tool or your developed mapping scripts generate status and error handling tables.

As a general principle, all ETL logs status and errors into a table. You monitor execution status using an ETL tool or by querying this log table directly.

Writing Source-ETL that Loads Efficiently

Whether you are developing mapping scripts and loading into a staging layer or directly into the foundation layer the goal is to get the data into the warehouse in the most expedient manner. In order to achieve good performance during the load you must begin by focusing on where the data to be loaded resides and how you load it into the database. For example, you should not use a serial database link or a single JDBC connection to move large volumes of data. The most common and preferred mechanism for loading large volumes of data is loading from flat files.

The following topics discuss best practices for ensuring your source-ETL loads efficiently:

- Using a Staging Area for Flat Files

- Preparing Raw Data Files for Source-ETL

- Source-ETL Data Loading Options

- Parallel Direct Path Load Source-ETL

- Partition Exchange Load for Oracle Utilities Data Model Source-ETL

Using a Staging Area for Flat Files

The area where flat files are stored before being loaded into the staging layer of a data warehouse system is commonly known as staging area. The overall speed of your load is determined by:

- How quickly the raw data can be read from staging area.

- How quickly the raw data can be processed and inserted into the database.

Recommendations: Using a Staging Area

Stage the raw data across as many physical disks as possible to ensure that reading it is not a bottleneck during the load.

Also, if you are using the Exadata Database Machine, the best place to stage the data is in an Oracle Database File System (DBFS) stored on the Exadata storage cells. DBFS creates a mountable cluster file system which can you can use to access files stored in the database. Create the DBFS in a separate database on the Database Machine. This allows the DBFS to be managed and maintained separately from the data warehouse.

Mount the file system using the DIRECT_IO option to avoid thrashing the system page cache while moving the raw data files in and out of the file system.

See:
Oracle Database SecureFiles and Large Objects Developer's Guide for more information on setting up DBFS.

Preparing Raw Data Files for Source-ETL

In order to parallelize the data load Oracle Database must be able to logically break up the raw data files into chunks, known as granules. To ensure balanced parallel processing, the number of granules is typically much higher than the number of parallel server processes. At any given point in time, a parallel server process is allocated one granule to work on. After a parallel server process completes working on its granule, another granule is allocated until all of the granules are processed and the data is loaded.

Recommendations: Preparing Raw Data Files for Source-ETL

Follow these recommendations:

- Deliminate each row using a known character such as a new line or a semicolon. This ensures that Oracle can look inside the raw data file and determine where each row of data begins and ends in order to create multiple granules within a single file.

- If a file is not position-able and seek-able (for example the file is compressed or zip file), then the files cannot be broken up into granules and the whole file is treated as a single granule. In this case, only one parallel server process can work on the entire file. In order to parallelize the loading of compressed data files, use multiple compressed data files. The number of compressed data files used determines the maximum parallel degree used by the load.

- When loading multiple data files (compressed or uncompressed):

- Use a single external table, if at all possible

- Make the files similar in size

- Make the size of the files a multiple of 10 MB

- If you must have files of different sizes, list the files from largest to smallest. By default, Oracle assumes that the flat file has the same character set as the database. If this is not the case, specify the character set of the flat file in the external table definition to ensure the proper character set conversions can take place.

Source-ETL Data Loading Options

Oracle offers several data loading options

- External table or SQL*Loader

- Oracle Data Pump (import and export)

- Change Data Capture and Trickle feed mechanisms (such as Oracle GoldenGate)

- Oracle Database Gateways to open systems and mainframes

- Generic Connectivity (ODBC and JDBC)

The approach that you take depends on the source and format of the data you receive.

Recommendations: Loading Flat Files

If you are loading from files into Oracle you have two options: SQL*Loader or external tables.

Using external tables offers the following advantages:

- Allows transparent parallelization inside the database.You can avoid staging data and apply transformations directly on the file data using arbitrary SQL or PL/SQL constructs when accessing external tables. SQL Loader requires you to load the data as-is into the database first.

- Parallelizing loads with external tables enables a more efficient space management compared to SQL*Loader, where each individual parallel loader is an independent database sessions with its own transaction. For highly partitioned tables this could potentially lead to a lot of wasted space.

You can create an external table using the standard CREATE TABLE statement. However, to load from flat files the statement must include information about where the flat files reside outside the database. The most common approach when loading data from an external table is to issue a CREATE TABLE AS SELECT (CTAS) statement or an INSERT AS SELECT (IAS) statement into an existing table.

Parallel Direct Path Load Source-ETL

A direct path load parses the input data according to the description given in the external table definition, converts the data for each input field to its corresponding Oracle data type, then builds a column array structure for the data. These column array structures are used to format Oracle data blocks and build index keys. The newly formatted database blocks are then written directly to the database, bypassing the standard SQL processing engine and the database buffer cache.

The key to good load performance is to use direct path loads wherever possible:

- A CREATE TABLE AS SELECT (CTAS) statement always uses direct path load.

- A simple INSERT AS SELECT (IAS) statement does not use direct path load. In order to achieve direct path load with an IAS statement you must add the APPEND hint to the command.

Direct path loads can also run in parallel. To set the parallel degree for a direct path load, either:

- Add the PARALLEL hint to the CTAS statement or an IAS statement.

- Set the PARALLEL clause on both the external table and the table into which the data is loaded.
After the parallel degree is set:

- A CTAS statement automatically performs a direct path load in parallel.

- An IAS statement does not automatically perform a direct path load in parallel. In order to enable an IAS statement to perform direct path load in parallel, you must alter the session to enable parallel DML by executing the following statement.

alter session enable parallel DML;

Partition Exchange Load for Oracle Utilities Data Model Source-ETL

A benefit of partitioning is the ability to load data quickly and easily with minimal impact on the business users by using the EXCHANGE PARTITION command. The EXCHANGE PARTITION command enables swapping the data in a nonpartitioned table into a particular partition in your partitioned table. The EXCHANGE PARTITION command does not physically move data, instead it updates the data dictionary to exchange a pointer from the partition to the table and vice versa.

Because there is no physical movement of data, an exchange does not generate redo and undo. In other words, an exchange is a sub-second operation and far less likely to impact performance than any traditional data-movement approaches such as INSERT.

Recommendations: Partitioning Tables

Partition the larger tables and fact tables in the Oracle Utilities Data Model warehouse.

Example 4-1 Using Exchange Partition Statement with a Partitioned Table

Assume that there is a large table called Sales, which is range partitioned by day. At the end of each business day, data from the online sales system is loaded into the Sales table in the warehouse.

The following steps ensure the daily data gets loaded into the correct partition with minimal impact to the business users of the data warehouse and optimal speed:

1. Create external table for the flat file data coming from the online system

2. Using a CTAS statement, create a nonpartitioned table called tmp_sales that has the same column structure as Sales table

3. Build any indexes that are on the Sales table on the tmp_sales table

4. Issue the EXCHANGE PARTITION command.

Alter table Sales exchange partition p2 with
table top_sales including indexes without validation;

5. Gather optimizer statistics on the newly exchanged partition using incremental statistics.

The EXCHANGE PARTITION command in this example, swaps the definitions of the named partition and the tmp_sales table, so the data instantaneously exists in the right place in the partitioned table. Moreover, with the inclusion of the INCLUDING INDEXES and WITHOUT VALIDATION clauses, Oracle swaps index definitions and does not check whether the data actually belongs in the partition - so the exchange is very quick.

Note:
The assumption being made in this example is that the data integrity was verified at date extraction time. If you are unsure about the data integrity, omit the WITHOUT VALIDATION clause so that the Database checks the validity of the data.

Customizing Intra-ETL for Oracle Utilities Data Model

The Oracle Utilities Data Model uses workflow implemented using PL/SQL packages to execute the intra-ETL process. The workflow consists of three major components:

1. Executing Derived Intra-ETL Programs:

1. Independent Derived intra-ETL programs - Level 0

2. First level dependent Derived intra-ETL programs - Level 1

2. Refreshing Aggregate Materialized Views:

3. Refreshing OLAP Cubes

Figure 4-2 illustrates the Oracle Utilities Data Model intra-ETL workflow.

Figure 4-2 Oracle Utilities Data Model Intra-ETL Workflow

Executing Derived Intra-ETL Programs

The first component of the workflow to execute the intra-ETL process is the Derived intra-ETL programs. This component has two subcomponents to deal with the dependency among Derived intra-ETL programs:

- The first subcomponent, Independent Derived intra-ETL programs, has Derived intra-ETL programs that get data from foundation layer tables, that is, base, lookup, and reference tables.

- The second subcomponent has Derived intra-ETL programs that depend on the first subcomponent, Independent Derived intra-ETL programs. The second subcomponent intra-ETL programs get data from foundation layer tables, that is, base, lookup, and reference tables and also from derived tables that have intra-ETL programs in first subcomponent.

The Intra-ETL programs in the two subcomponents are implemented using PL/SQL packages. All Intra-ETL packages insert data for the ETL period mentioned in DWC_ETL_PARAMTER table for "OUDM-INTRA-ETL" process. Modify the ETL period of the process as per the data load requirements. If you are trying to load data for ETL period, for which data is already loaded, intra-ETL program first truncates the partitions existing for the ETL period, and then loads data into the target derived table.

Refreshing Aggregate Materialized Views

This is the second component of the workflow. This component depends on the first component, Executing Derived intra-ETL programs. The execution of this component happens only when the execution of the first component completes successfully.

Refreshing OLAP Cubes

This is the third component of the workflow. This component depends on the second component, Refreshing Aggregate materialized views, which in turn depends on the first component, Executing Derived intra-ETL programs. The execution of this component happens only when the execution of the second component completes successfully. This component refreshes data in OLAP cubes and dimensions based on the parameters given in DWC_OLAP_ETL_PARAMETER table.

Executing Intra-ETL Workflow

Oracle Utilities Data Model intra-ETL workflow is implemented using a PL/SQL package, PKG_INTRA_ETL_PROCESS. Each component and their subcomponents of intra-ETL workflow have one procedure each. All these procedures are private to the package. The package has only one public procedure, which invokes all private procedures as depicted in the workflow figure. Before executing the workflow, ensure that you set all ETL parameters in DWC_OLAP_PARAMTER and DWC_OLAP_ETL_PARAMETER tables. Invoking PKG_INTRA_ETL_PROCESS.RUN procedure starts the workflow execution. Further sections explain about how to check the status of each activity and managing errors occur in workflow execution.

Performing an Initial Load of an Oracle Utilities Data Model Warehouse

Performing an initial load of an Oracle Utilities Data Model is a multistep process:

1. Load the foundation layer of the Oracle Utilities Data Model warehouse (that is, the reference, lookup, and base tables) as described in "Performing an Initial Load of the Foundation Layer".

2. Load the access layer of the Oracle Utilities Data Model warehouse (that is, the derived and aggregate tables, materialized views, OLAP cubes, and data mining models) as described in "Performing an Initial Load of the Access Layer".

Performing an Initial Load of the Foundation Layer

Perform initial load of the foundation layer using source-ETL that you create. See "Writing Your Own Source-ETL"for more information on creating this ETL.

Performing an Initial Load of the Access Layer

To perform an initial load of access layer of the Oracle Utilities Data Model warehouse (that is, the derived and aggregate tables, materialized views, OLAP cubes) take the following steps:

1. Update the parameters in DWC_ETL_PARAMETER control table in the oudm_sys schema for different processes so that the ETL can use this information (that is, the beginning and end date of the ETL period) when loading the derived and aggregate tables and views.
For an initial load of an Oracle Utilities Data Model warehouse, specify the values shown in the following tables:

For OUDM-INTRA-ETL process:

Columns

Value

PROCESS_NAME

'OUDM-INTRA-ETL'

FROM_DATE_ETL

The beginning date of the ETL period.

TO_DATE_ETL

The ending date of the ETL period.

For more information on DWC_ETL_PARAMETER control table, see Oracle Utilities Data Model Reference.

2. Update the Oracle Utilities Data Model OLAP ETL parameters in DWC_OLAP_ETL_PARAMETER control table in the oudm_sys schema to specify the build method and other build characteristics so that the ETL can use this information when loading the OLAP cube data.
For an initial load of the analytic workspace, specify values following the guidelines in Table 4-1.

Table 4-1 Values of OLAP ETL Parameters in the DWC_OLAP_ETL_PARAMETER table for Initial Load

Column Name

Value

PROCESS_NAME

' OUDM-OLAP-ETL'

BUILD_METHOD

C which specifies a complete refresh which clears all dimension values before loading.

CUBENAME

One of the following values that specifies the cubes you want to build:

- ALL specifies a build of the cubes in the Oracle Utilities Data Model analytic workspace.

- cubename[[|cubename]...] specifies one or more cubes to build.

MAXJOBQUEUES

A decimal value that specifies the number of parallel processes to allocate to this job. (Default value is 4.) The value that you specify varies depending on the setting of the JOB_QUEUE_PROCESSES database initialization parameter.

CALC_FCST

One of the following values depending on whether you want to calculate forecast cubes:

- Y specifies calculate forecast cubes.

- N specifies do not calculate forecast cubes.

NO_FCST_YRS

If the value for the CALC_FCST column is Y, specify a decimal value that specifies how many years forecast data you want to calculate; otherwise, specify NULL.

FCST_MTHD

If the value for the CALC_FCST column is Y, then specify AUTO; otherwise, specify NULL.

FCST_ST_YR

If the value for the CALC_FCST column is Y, then specify value specified as 'BY YYYY' which is the "start business year" of a historical period; otherwise, specify NULL.

FCST_END_YR

If the value for the CALC_FCST column is Y, then specify value specified as 'BY YYYY' which is the "end business year" of a historical period; otherwise, specify NULL.

OTHER1

Specify NULL.

OTHER2

Specify NULL.

3. Execute the intra-ETL as described in "Executing the Default Oracle Utilities Data Model Intra-ETL".

For more information, see "Refreshing the Data in an Oracle Utilities Data Model Warehouse".

Executing the Default Oracle Utilities Data Model Intra-ETL

Intra-ETL workflow is implemented using PL/SQL package, PKG_INTRA_ETL_PROCESS. This package has a public procedure, Run, and also has private procedures for executing derived intra-ETL programs, refreshing aggregate materialized views, and refreshing OLAP cubes. The public procedure, Run, invokes all private procedures.

Before executing intra-ETL workflow, update ETL parameters in DWC_ETL_PARAMETER and DWC_OLAP_ETL_PARAMETER tables. It is suggested to not use oudm_sys user to update ETL parameter tables and executing intra-ETL workflow. Ask your DBA to create a new user for performing these tasks using following commands:

CREATE USER oudm_user IDENTIFIED BY oudm_user;
GRANT CREATE SESSION TO oudm_user;
GRANT ALTER SESSION TO oudm_user;

GRANT EXECUTE ON oudm_sys.PKG_INTRA_ETL_PROCESS TO oudm_user;
GRANT EXECUTE ON oudm_sys.PKG_INTRA_ETL_UTIL TO oudm_user;

GRANT SELECT,UPDATE ON oudm_sys.DWC_ETL_PARAMETER TO oudm_user;
GRANT SELECT ON oudm_sys.DWC_INTRA_ETL_ACTIVITY TO oudm_user;
GRANT SELECT ON oudm_sys.DWC_INTRA_ETL_PROCESS TO oudm_user;
GRANT SELECT,UPDATE ON oudm_sys.DWC_OLAP_ETL_PARAMETER TO oudm_user;
GRANT SELECT ON oudm_sys.DWC_OLAP_ACTIVITY TO oudm_user;
GRANT SELECT ON oudm_sys.DWC_MESSAGE TO oudm_user;

Use oudm_user user to update ETL parameter tables and execute intra-ETL workflow. In a SQLPLUS session, connect to oudm_user user:

sqlplus oudm_user/oudm_user@SID

Update ETL parameter tables:

SQL> UPDATE DWC_ETL_PARAMETER
SET from_date_etl = < The beginning date of the ETL period >,
to_date_etl   = < The ending date of the ETL period >
WHERE process_name = 'OUDM-INTRA-ETL'
;
/
SQL> commit;

SQL> UPDATE DWC_OLAP_ETL_PARAMETER
SET build_method = <>,
cubename   = <>,
.
.
.
.
fcst_st_yr = <>,
fcst_end_yr = <>
;
/
SQL> commit;

Run the following command to execute intra-ETL workflow:

SQL> BEGIN
OUDM_SYS.PKG_INTRA_ETL_PROCESS.Run;
END;
/

The status of each activity is tracked using DWC_INTRA_ETL_ACTIVITY table. The status of each cube data loading is tracked using DWC_OLAP_ACTIVITY table. The status of the entire intra-ETL workflow process is tracked using DWC_INTRA_ETL_PROCESS table. See "Monitoring the Execution of the Intra-ETL Process" for more information on these tables.

Refreshing the Data in an Oracle Utilities Data Model Warehouse

The section, "Performing an Initial Load of the Access Layer" describes how to perform an initial load of an Oracle Utilities Data Model data warehouse. After this initial load, you must load new data into your Oracle Utilities Data Model data warehouse regularly so that it can serve its purpose of facilitating business analysis.

To load new data into your Oracle Utilities Data Model warehouse, you extract the data from one or more operational systems and copy that data into the warehouse. The challenge in data warehouse environments is to integrate, rearrange and consolidate large volumes of data over many systems, thereby providing a new unified information base for business intelligence.

The successive loads and transformations must be scheduled and processed in a specific order that is determined by your business needs. Depending on the success or failure of the operation or parts of it, the result must be tracked and subsequent, alternative processes might be started.

You can do a full incremental load of the Oracle Utilities Data Model warehouse, or you can refresh the data sequentially, as follows:

1. Refreshing the Foundation Layer of Oracle Utilities Data Model Warehouse

2. Refreshing the Access Layer of an Oracle Utilities Data Model Warehouse

In either case, you can manage errors during the execution of the intra-ETL, as described in "Managing Errors During Oracle Utilities Data Model Intra-ETL Execution".

Refreshing the Foundation Layer of Oracle Utilities Data Model Warehouse

You can refresh the foundation layer of an Oracle Utilities Data Model warehouse (that is, the reference, lookup, and base tables) in the following ways:

- You can refresh the foundation layer using source-ETL scripts that you wrote using Oracle Warehouse Builder or another ETL tool. For more information on creating source-ETL, see "Writing Your Own Source-ETL".

Refreshing the Access Layer of an Oracle Utilities Data Model Warehouse

Refreshing the access layer of an Oracle Utilities Data Model is a multi-step process. You can do a full incremental load of the access layer all at one time, or you can refresh the data sequentially, as follows:

1. Refreshing Oracle Utilities Data Model Derived Tables

2. Refreshing Oracle Utilities Data Model Aggregate Materialized Views

3. Refreshing Oracle Utilities Data Model OLAP Cubes

In either case, you can manage errors during the execution of the intra-ETL as described in "Managing Errors During Oracle Utilities Data Model Intra-ETL Execution".

To accomplish incremental loading of Oracle Utilities Data Model data warehouse, ask your DBA to grant execute privilege on Derived intra-ETL, OLAP ETL, and Mining PL/SQL packages and select privilege on a few tables:

GRANT EXECUTE ON oudm_sys.PKG_DWD_ACCT_ARRER_MO TO oudm_user;
GRANT EXECUTE ON oudm_sys.PKG_DWD_ACCT_BAL_MO TO oudm_user;
GRANT EXECUTE ON oudm_sys.PKG_DWD_ACCT_DEBT_DAY TO oudm_user;
GRANT EXECUTE ON oudm_sys.PKG_DWD_ACCT_PMT_MTD_STAT_HST TO oudm_user;
GRANT EXECUTE ON oudm_sys.PKG_DWD_ACCT_PYMT_DAY TO oudm_user;
GRANT EXECUTE ON oudm_sys.PKG_DWD_END_DVC_EVT_CUST_DAY TO oudm_user;
GRANT EXECUTE ON oudm_sys.PKG_DWD_END_DVC_EVT_DVC_DAY TO oudm_user;
GRANT EXECUTE ON oudm_sys.PKG_DWD_MTR_RDNG_DAY TO oudm_user;
GRANT EXECUTE ON oudm_sys.PKG_DWD_MTR_RDNG_HR TO oudm_user;
GRANT EXECUTE ON oudm_sys.PKG_DWD_OUTG_DAY TO oudm_user;
GRANT EXECUTE ON oudm_sys.PKG_DWD_OUTG_USG_PNT TO oudm_user;
GRANT EXECUTE ON oudm_sys.PKG_DR_PROG_LD_RDCTN_RGN_DAY TO oudm_user;
GRANT EXECUTE ON oudm_sys.PKG_DWD_RLBLTY_IND_CITY_MO TO oudm_user;
GRANT EXECUTE ON oudm_sys.PKG_DWD_RLBLTY_IND_FEDR_MO TO oudm_user;
GRANT EXECUTE ON oudm_sys.PKG_DWD_CUST_DR_PROG_PROFILE TO oudm_user;
GRANT EXECUTE ON oudm_sys.PKG_MINING_ETL TO oudm_user;
GRANT EXECUTE ON oudm_sys.PKG_OUDM_MINING TO oudm_user;
GRANT EXECUTE ON oudm_sys.PKG_OUDM_OLAP_ETL_AW_LOAD TO oudm_user;
GRANT SELECT ON oudm_sys.DWR_DEMAND_RESPN_PROG TO oudm_user;
GRANT SELECT ON oudm_sys.USER_MINING_MODELS TO oudm_user;

Refreshing Oracle Utilities Data Model Derived Tables

Refreshing the relational tables in an Oracle Utilities Data Model is a multi-step process:

1. Refresh the foundation layer of the Oracle Utilities Data Model warehouse (that is, the reference, lookup, and base tables) with operational system data by executing the source-ETL that you have written.

2. Update the parameters of the DWC_ETL_PARAMETER control table for 'OUDM-INTRA-ETL' process. Please refer to "Performing an Initial Load of an Oracle Utilities Data Model Warehouse" for more information on the DWC_ETL_PARAMETER table. For an incremental load of an Oracle Utilities Data Model warehouse, specify the values shown in the following table (that is, the beginning and end date of the ETL period) for all three processes

Columns

Value

FROM_DATE_ETL

The beginning date of the ETL period.

TO_DATE_ETL

The ending date of the ETL period.

For more information on DWC_ETL_PARAMETER control table, see Oracle Utilities Data Model Reference.

3. Create a session by connecting oudm_user user through SQLPLUS. Then, start an intra-ETL process. Make sure the previous process ended with 'COMPLETED-SUCCESS' status before starting a new process:

sqlplus oudm_user/oudm_user@SID

SQL> DECLARE
l_process_type  OUDM_SYS.DWC_INTRA_ETL_PROCESS.PROCESS_TYPE%TYPE;
l_error_text    OUDM_SYS.DWC_MESSAGE.MESSAGE_TEXT%TYPE;
l_process_no    NUMBER;
BEGIN
l_process_no := OUDM_SYS.PKG_INTRA_ETL_UTIL.Start_Process(l_process_type,l_error_text);
END;
/

4. Refresh Oracle Utilities Data Model derived tables by executing following commands:

SQL> DECLARE
p_process_no    NUMBER;
l_status        VARCHAR2(20);
BEGIN
l_status :=
OUDM_SYS.PKG_DWD_ACCT_ARRER_MO.Load('DWD_ACCT_ARRER_MO',p_process_no);
l_status :=
OUDM_SYS.PKG_DWD_ACCT_BAL_MO.Load('DWD_ACCT_BAL_MO',p_process_no);
l_status :=
OUDM_SYS.PKG_DWD_ACCT_DEBT_DAY.Load('DWD_ACCT_DEBT_DAY',p_process_no);
l_status := OUDM_SYS.PKG_DWD_ACCT_PMT_MTD_STAT_HST.Load('DWD_ACCT_PYMT_MTHD_STAT_HIST',p_process_no);
l_status :=
OUDM_SYS.PKG_DWD_ACCT_PYMT_DAY.Load('DWD_ACCT_PYMT_DAY',p_process_no);
l_status := OUDM_SYS.PKG_DWD_END_DVC_EVT_CUST_DAY.Load('DWD_END_DVC_EVT_CUST_DAY',p_process_no);
l_status :=   OUDM_SYS.PKG_DWD_END_DVC_EVT_DVC_DAY.Load('DWD_END_DVC_EVT_DVC_DAY',p_process_no);
l_status :=
OUDM_SYS.PKG_DWD_MTR_RDNG_DAY.Load('DWD_MTR_RDNG_DAY',p_process_no);
l_status :=
OUDM_SYS.PKG_DWD_MTR_RDNG_HR.Load('DWD_MTR_RDNG_HR',p_process_no);
l_status :=
OUDM_SYS.PKG_DWD_OUTG_DAY.Load('DWD_OUTG_DAY',p_process_no);
l_status :=
OUDM_SYS.PKG_DWD_OUTG_USG_PNT.Load('DWD_OUTG_USG_PNT',p_process_no);
END;
/

SQL> DECLARE
p_process_no    NUMBER;
l_status        VARCHAR2(20);
BEGIN
l_status := OUDM_SYS.PKG_DR_PROG_LD_RDCTN_RGN_DAY.Load ('DWD_DR_PROG_LD_RDCTN_RGN_DAY',p_process_no);
l_status := OUDM_SYS.PKG_DWD_RLBLTY_IND_CITY_MO.Load('DWD_RLBLTY_IND_CITY_MO',p_process_no);
l_status := OUDM_SYS.PKG_DWD_RLBLTY_IND_FEDR_MO.Load('DWD_RLBLTY_IND_FEDR_MO',p_process_no);
END;
/

Refreshing Oracle Utilities Data Model Aggregate Materialized Views

Refreshing the Aggregate Materialized Views in an Oracle Utilities Data Model is a multi-step process:

1. Refresh the foundation layer of the Oracle Utilities Data Model warehouse (that is, the reference, lookup, and base tables) with operational system data by executing the source-ETL that you have written.

2. Refresh Oracle Utilities Data Model derived tables as explained in "Refreshing Oracle Utilities Data Model Derived Tables".

3. Create a session by connecting oudm_user user through SQLPLUS. An intra-ETL process created in "Refreshing Oracle Utilities Data Model Derived Tables" must be in 'RUNNING' status now:

sqlplus oudm_user/oudm_user@SID

4. Refresh Oracle Utilities Data Model aggregate materialized views by executing following commands:

SQL> DECLARE
p_process_no    NUMBER;
l_status        VARCHAR2(20);
BEGIN
l_status := OUDM_SYS.PKG_INTRA_ETL_UTIL.Refresh_MV('DWA_END_DVC_EVT_DVC_MO',p_process_no);
l_status := OUDM_SYS.PKG_INTRA_ETL_UTIL.Refresh_MV('DWA_END_DVC_EVT_CUST_MO',p_process_no);
l_status :=   OUDM_SYS.PKG_INTRA_ETL_UTIL.Refresh_MV('DWA_MTR_RDNG_MO',p_process_no);
l_status := OUDM_SYS.PKG_INTRA_ETL_UTIL.Refresh_MV('DWA_MTR_RDNG_TOU_MO',p_process_no);
l_status := OUDM_SYS.PKG_INTRA_ETL_UTIL.Refresh_MV('DWA_OUTG_MO',p_process_no);
l_status := OUDM_SYS.PKG_INTRA_ETL_UTIL.Refresh_MV('DWA_MTR_RDNG_MO_ACCT',p_process_no);
l_status := OUDM_SYS.PKG_INTRA_ETL_UTIL.Refresh_MV('DWA_MTR_RDNG_MO_CUST',p_process_no);
l_status := OUDM_SYS.PKG_INTRA_ETL_UTIL.Refresh_MV('DWA_MTR_RDNG_MO_UP',p_process_no);
END;
/

Refreshing Oracle Utilities Data Model OLAP Cubes

On a scheduled basis you must update the OLAP cube data with the relational data that has been added to the Oracle Utilities Data Model data warehouse since the initial load of the OLAP cubes. Refreshing the OLAP Cubes in an Oracle Utilities Data Model is a multi-step process:

1. Refresh the foundation layer of the Oracle Utilities Data Model warehouse (that is, the reference, lookup, and base tables) with operational system data by executing the source-ETL that you have written.

2. Refresh Oracle Utilities Data Model derived tables as explained in "Refreshing Oracle Utilities Data Model Derived Tables".

3. Refresh Oracle Utilities Data Model aggregate materialized views as explained in "Refreshing Oracle Utilities Data Model Aggregate Materialized Views".

4. Update the parameters of the DWC_OLAP_ETL_PARAMETER control table. See "Performing an Initial Load of an Oracle Utilities Data Model Warehouse" for information on updating the DWC_OLAP_ETL_PARAMETER table.
For more information on DWC_OLAP_ETL_PARAMETER control table, see Oracle Utilities Data Model Reference.

5. Create a session by connecting oudm_user user through SQLPLUS. An intra-ETL process created in "Refreshing Oracle Utilities Data Model Derived Tables" must be in 'RUNNING' status now:

sqlplus oudm_user/oudm_user@SID

6. Refresh Oracle Utilities Data Model OLAP cubes by executing following commands:

SQL> DECLARE
l_build_methd OUDM_SYS.DWC_OLAP_ETL_PARAMETER.BUILD_METHOD%TYPE;
l_cube_nm OUDM_SYS.DWC_OLAP_ETL_PARAMETER.CUBENAME%TYPE;
l_maxjobques OUDM_SYS.DWC_OLAP_ETL_PARAMETER.MAXJOBQUEUES%TYPE;
l_calc_fcst  OUDM_SYS.DWC_OLAP_ETL_PARAMETER.CALC_FCST%TYPE;
l_no_fcst_yrs OUDM_SYS.DWC_OLAP_ETL_PARAMETER.NO_FCST_YRS%TYPE;
l_fcst_mthd  OUDM_SYS.DWC_OLAP_ETL_PARAMETER.FCST_MTHD%TYPE;
l_fcst_st_yr OUDM_SYS.DWC_OLAP_ETL_PARAMETER.FCST_ST_YR%TYPE;
l_fcst_end_yr OUDM_SYS.DWC_OLAP_ETL_PARAMETER.FCST_END_YR%TYPE;
l_status VARCHAR2(20);
BEGIN
/***************  Fetching the values of the OLAP ETL parameters variable used in this procedure  ****************/
SELECT
BUILD_METHOD l_build_methd,
CUBENAME l_cube_nm,
MAXJOBQUEUES l_maxjobques,
CALC_FCST l_calc_fcst,
NO_FCST_YRS l_no_fcst_yrs,
FCST_MTHD l_fcst_mthd,
FCST_ST_YR l_fcst_st_yr,
FCST_END_YR l_fcst_end_yr
INTO
l_build_methd,
l_cube_nm,
l_maxjobques,
l_calc_fcst,
l_no_fcst_yrs,
l_fcst_mthd,
l_fcst_st_yr,
l_fcst_end_yr
FROM
OUDM_SYS.DWC_OLAP_ETL_PARAMETER;
l_status := OUDM_SYS.PKG_OUDM_OLAP_ETL_AW_LOAD.olap_etl_aw_build(l_build_methd,l_cube_nm,l_maxjobques,l_calc_fcst,l_no_fcst_yrs,l_fcst_mthd,l_fcst_st_yr,l_fcst_end_yr,null,null);
END;
/

7. If there is requirement to refresh only Oracle Utilities Data Model OLAP cubes, the same can be achieved with step 6, but before that make sure an intra-ETL process is already running. If no intra-ETL process is running, start one:

sqlplus oudm_user/oudm_user@SID

SQL> DECLARE
l_process_type  OUDM_SYS.DWC_INTRA_ETL_PROCESS.PROCESS_TYPE%TYPE;
l_error_text    OUDM_SYS.DWC_MESSAGE.MESSAGE_TEXT%TYPE;
l_process_no    NUMBER;
BEGIN
l_process_no := OUDM_SYS.PKG_INTRA_ETL_UTIL.Start_Process(l_process_type,l_error_text);
END;
/

Refreshing Oracle Utilities Data Model Data Mining Models

Refreshing of data mining models is not integrated into intra-ETL workflow. There is only one data mining model, Customer Savings and Customer Profile by DR Program. For each DR program, a mining model is trained. If you want to rebuild the mining models for all DR programs, perform the following steps:

1. Make sure an intra-ETL process is running. If not, start an intra-ETL process:

sqlplus oudm_user/oudm_user@SID

SQL> DECLARE
l_process_type  OUDM_SYS.DWC_INTRA_ETL_PROCESS.PROCESS_TYPE%TYPE;
l_error_text    OUDM_SYS.DWC_MESSAGE.MESSAGE_TEXT%TYPE;
l_process_no    NUMBER;
BEGIN
l_process_no := OUDM_SYS.PKG_INTRA_ETL_UTIL.Start_Process(l_process_type,l_error_text);
END;
/

2. Build (rebuild in case already built) mining model by executing the following commands:

sqlplus oudm_user/oudm_user@SID

DECLARE
--Indicates whether a model already built for a DR program can be rebuilt
l_model_refresh_ind CHAR(1) := 'Y';
l_model_exist_ind   CHAR(1);
BEGIN
FOR rec IN (SELECT DEMAND_RESPN_PROG_KEY FROM OUDM_SYS.DWR_DEMAND_RESPN_PROG WHERE END_DT < OUDM_SYS.PKG_INTRA_ETL_UTIL.Get_End_Date)
LOOP
SELECT COUNT(*) INTO l_model_exist_ind
FROM OUDM_SYS.USER_MINING_MODELS
WHERE MODEL_NAME = 'OUDM_PROFILE_KMEANS_' || rec.DEMAND_RESPN_PROG_KEY
;

IF l_model_exist_ind = 1
THEN
IF l_model_refresh_ind = 'Y'
THEN
OUDM_SYS.PKG_DWD_CUST_DR_PROG_PROFILE.loaddata(rec.DEMAND_RESPN_PROG_KEY);        OUDM_SYS.PKG_MINING_ETL.crt_cust_sgmnt_src_view(rec.DEMAND_RESPN_PROG_KEY);        OUDM_SYS.PKG_OUDM_MINING.crt_frst_step_cust_sgmnt_model(rec.DEMAND_RESPN_PROG_KEY);
ELSE
DBMS_OUTPUT.PUT_LINE('NOTE:Chosen to not refresh the existing model :: ' || 'OUDM_PROFILE_KMEANS_' || rec.DEMAND_RESPN_PROG_KEY || ' :: set l_model_refresh_ind to ''Y'' to refresh existing models');
END IF;

ELSE
OUDM_SYS.PKG_DWD_CUST_DR_PROG_PROFILE.loaddata(rec.DEMAND_RESPN_PROG_KEY);      OUDM_SYS.PKG_MINING_ETL.crt_cust_sgmnt_src_view(rec.DEMAND_RESPN_PROG_KEY);      OUDM_SYS.PKG_OUDM_MINING.crt_frst_step_cust_sgmnt_model(rec.DEMAND_RESPN_PROG_KEY);

END IF;
END LOOP;
END;
/

Managing Errors During Oracle Utilities Data Model Intra-ETL Execution

This topic discusses how you can identify and manage errors during intra-ETL execution. It contains the following topics:

- Monitoring the Execution of the Intra-ETL Process

- Recovering an Intra ETL Process

Monitoring the Execution of the Intra-ETL Process

Three oudm_sys schema control tables, DWC_INTRA_ETL_PROCESS, DWC_INTRA_ETL_ACTIVITY, DWC_OLAP_ACTIVITY monitor the execution of the intra-ETL process. These tables are documented in Oracle Utilities Data Model Reference. You can access these three tables from oudm_user user.

Each normal run (as opposed to an error-recovery run) of a separate intra-ETL execution performs the following steps:

1. Inserts a record into the DWC_INTRA_ETL_PROCESS table with a monotonically increasing system generated unique process key, SYSDATE as process start time, RUNNING as the process status, and an input date range in the FROM_DATE_ETL and TO_DATE_ETL columns.

2. Invokes each of the individual intra-ETL programs in the appropriate order of dependency. Before the invocation of each program, the procedure inserts a record into the intra-ETL Activity detail table, DWC_INTRA_ETL_ACTIVITY, with values for:

- ACTIVITY_KEY, a system generated unique activity key.

- PROCESS_KEY, the process key value corresponding to the intra-ETL process.

- ACTIVITY_NAME, an individual program name.

- ACTIVITY_DESC, a suitable activity description.

- ACTIVITY_START_TIME, the value of SYSDATE.

- ACTIVITY_STATUS, the value of RUNNING.

4. Updates the corresponding record in the DWC_INTRA_ETL_ACTIVITY table for the activity end time and activity status after the completion of each individual ETL program (either successfully or with errors). For successful completion of the activity, the procedure updates the status as 'COMPLETED-SUCCESS'. When an error occurs, the procedure updates the activity status as 'COMPLETED-ERROR', and also updates the corresponding error detail in the ERROR_DTL column.

5. Updates the record corresponding to the process in the DWC_INTRA_ETL_ PROCESS table for the process end time and status, after the completion of all individual intra-ETL programs. When all the individual programs succeed, the procedure updates the status to 'COMPLETED-SUCCESS'; otherwise it updates the status to 'COMPLETED-ERROR'.

6. For OLAP cubes loading, a record is inserted into DWC_OLAP_ACTIVITY table with CUBENAME as cube name, status as 'RUNNING', and LOAD_START_DT as SYSDATE for each cube. It updates the record upon the completion of cube loading. It updates STATUS column to 'COMPLETED-SUCCESS' if cube loading is successful, otherwise 'COMPLETE-ERROR' and updates LOAD_END_DT column to SYSDATE. In case of 'COMPLETED-ERROR' cubes, it also updates ERROR_DTL column with error details.

You can monitor the execution state of the intra-ETL, including current process progress, time taken by individual programs, or the complete process, by viewing the contents of the DWC_INTRA_ETL_PROCESS, DWC_INTRA_ETL_ACTIVITY, and DWC_OLAP_ACTIVITY tables. In DWC_INTRA_ETL_ACTIVITY table, see the records of currently running process. Monitoring can be done both during and after the execution of the intra-ETL procedure.

Recovering an Intra ETL Process

To recover an intra-ETL process

1. Identify the errors by looking at the corresponding error details that are tracked against the individual programs in the DWC_INTRA_ETL_ACTIVITY table.

2. Identify errors of OLAP cubes loading for individual cubes in DWC_OLAP_ACTIVITY table.

3. Correct the causes of the errors.

4. Re-invoke the intra-ETL process.

The intra-ETL workflow process identifies whether it is a normal run or recovery run by referring the DWC_INTRA_ETL_ACTIVITY table. During a recovery run, the intra-ETL workflow executes only the necessary programs. For example, for a derived population error as a part of the previous run, this recovery run executes the individual derived population programs which produced errors in the previous run. After their successful completion, the run refreshes aggregate materialized views in the appropriate order.

In this way, the intra-ETL error recovery is almost transparent, without involving the data warehouse or ETL administrator. The administrator must only correct the causes of the errors and re-invoke the intra-ETL process. The intra-ETL process identifies and executes the programs that generated errors.

5 Report and Query Customization

This chapter provides information about creating reports, queries, and dashboards against the data in an Oracle Utilities Data Model warehouse. It contains the following topics:

- Reporting Approaches in Oracle Utilities Data Model

- Customizing Oracle Utilities Data Model Sample Reports

- Writing Your Own Queries and Reports

- Optimizing Star Queries

- Troubleshooting Oracle Utilities Data Model Report Performance

- Writing As Is and As Was Queries

- Tutorial: Creating a New Oracle Utilities Data Model Dashboard

- Tutorial: Creating a New Oracle Utilities Data Model Report

Reporting Approaches in Oracle Utilities Data Model

There are two main approaches to creating reports from data in an Oracle Utilities Data Model warehouse: Relational Reporting and OLAP Reporting.

Relational Reporting

With relational reporting, you create reports against the analytical layer entities using the fact entities as the center of the star with the reference entities (that is, DWR_ and DWL_ tables) acting as the dimensions of the star. Typically the fact entities include the derived and aggregate entities (that is, DWD_ and DWA_ tables). However in some cases, you may need to use the base entities (that is, DWB_ tables) along with the reference tables to generate more detailed reports.

The reference tables (that is, DWR_ tables) typically represent dimensions which contain a business hierarchy and are present in the form of snowflake entities containing a table for each level of the hierarchy. This allows us to attach the appropriate set of reference entities for the multiple subject area and fact entities composed of differing granularity.

For example, you can use the set of tables comprising DWR_DAY tables to query against a DAY level meter reading entity such as DWD_MTR_RDNG_DAY. On the other hand, you need to use the higher level snowflakes at Month level and above such as DWR_CLNDR_MO, DWR_CLNDR_QTR, DWR_CLNDR_YR in order to query against the MONTH level meter reading entity such as DWA_MTR_RDNG_MO.

The lookup tables (that is tables, with the DWL_ prefix) represent the simpler dimensions comprising a single level containing a flat list of values. Typically, most reporting tools add a superficial top level to the dimension.

Note:
The use of numbers as text in Lookup code allows you to group them by using only the first character of the lookup value code. This could provide an artificial hierarchy level.

OLAP Reporting

With OLAP reporting, you access Oracle OLAP cubes using SQL against the dimension and cube (fact) views. Cubes and dimensions are represented using a star schema design. Dimension views form a constellation around the cube (or fact) view. The dimension and cube views are relational views with names ending with _VIEW. Typically, the dimension view used in the reports is named dimension_hierarchy_VIEW and the cube view is named cube_VIEW.

Unlike the corresponding relational dimension entities stored in DWR_ tables, the OLAP dimension views contains information relating to the whole dimension including all the levels of the hierarchy logically partitioned on the basis of a level column (identified as level_name). On a similar note, the cube views also contain the facts pertaining to the cross-combination of the levels of individual dimensions which are part of the cube definition. Also the join from the cube view and the dimension views are based on the dimension keys along with required dimension level filters.

Although the OLAP views are also modeled as a star schema, there are certain unique features to the OLAP reporting methodology which requires special modeling techniques in Oracle Business Intelligence Suite Enterprise Edition.

See also:
The Oracle By Example tutorial, entitled "Using Oracle OLAP 11g With Oracle BI Enterprise Edition". To access the tutorial, open the Oracle Learning Library in your browser by following the instructions in"Oracle Technology Network" ; and, then, search for the tutorials by name.

The rest of this chapter explains how to create Oracle Utilities Data Model reports. For examples of Oracle Utilities Data Model reports, see:

- Writing As Is and As Was Queries

- Tutorial: Creating a New Oracle Utilities Data Model Dashboard

- Tutorial: Creating a New Oracle Utilities Data Model Report

- The sample reports provided with Oracle Utilities Data Model that are documented in Oracle Utilities Data Model Reference.

Customizing Oracle Utilities Data Model Sample Reports

Sample reports and dashboards are delivered with Oracle Utilities Data Model. These sample reports illustrate the analytic capabilities provided with Oracle Utilities Data Model -- including the OLAP and data mining capabilities.

See:
Oracle Utilities Data Model Installation Guide for more information on installing the sample reports and deploying the Oracle Utilities Data Model RPD and webcat on the Business Intelligence Suite Enterprise Edition instance.

The sample reports were developed using Oracle Business Intelligence Suite Enterprise Edition which is a comprehensive suite of enterprise business intelligence products that delivers a full range of analysis and reporting capabilities. Thus, the reports also illustrate the ease with which you can use Oracle Business Intelligence Suite Enterprise Edition Answers and Dashboard presentation tools to create useful reports.

You can use Oracle Business Intelligence Suite Enterprise Edition Answers and Dashboard presentation tools to customize the predefined sample dashboard reports:

- Oracle BI Answers. Provides end user ad hoc capabilities in a pure Web architecture. Users interact with a logical view of the information -- completely hidden from data structure complexity while simultaneously preventing runaway queries. Users can easily create charts, pivot tables, reports, and visually appealing dashboards.

- Oracle BI Interactive Dashboards. Provide any knowledge worker with intuitive, interactive access to information. The end user can be working with live reports, prompts, charts, tables, pivot tables, graphics, and tickers. The user has full capability for drilling, navigating, modifying, and interacting with these results.

See:
Oracle Utilities Data Model Reference for detailed information on the sample reports.

Writing Your Own Queries and Reports

The oudm_sys schema defines the relational tables and views in Oracle Utilities Data Model. You can use any SQL reporting tool to query and report on these tables and views.

Oracle Utilities Data Model also supports On Line Analytic processing (OLAP) reporting using OLAP cubes defined in the oudm_sys schema. You can query and write reports on OLAP cubes by using SQL tools to query the views that are defined for the cubes or by using OLAP tools to directly query the OLAP components.

See also:
Reporting Approaches in Oracle Utilities Data Model, "Oracle OLAP Cube Views", and the discussion on querying dimensional objects in Oracle OLAP User's Guide.

Example 5-1 Creating a Relational Query for Oracle Utilities Data Model

For example, assume that you want to know the total kilowatt usage details for the top ten customers for March 2006. To answer this question, you might have to query the tables described in the following table.

Entity Name

Table Name

Description

METER READING BY DAY

DWD_MTR_RDNG_DAY

Derived fact table on daily meter reading

CUSTOMER

DWR_CUST

All the customers, including individual and organization customers

DAY

DWR_DAY

Calendar day in the day dimension.

To perform this query, you execute the following SQL statement.

SELECT *
FROM
(SELECT c.prty_dscr as Customer,
SUM( m.tot_kwh ) as Total_KWH
FROM dwd_mtr_rdng_day m,
dwr_day d,
dwr_cust c
WHERE m.day_key = d.day_key
AND m.cust_key  = c.cust_key
AND TO_CHAR(to_date(m.clndr_mo_key, 'yyyymmdd'), 'MON-YY') LIKE 'MAR-06'
GROUP BY c.prty_dscr,
TO_CHAR(to_date(m.clndr_mo_key, 'yyyymmdd'), 'MON-YY')
ORDER BY 3 DESC
)
WHERE ROWNUM <= 10;

The result of this query:

CUSTOMER            TOTAL_KWH
Brady Bakker        1152.58
Deb Abbassi         1120.201
Charli Eddisson     1119.701
Jasmine Seto        1111.391
Mason Murray        1109.741
Reuben Zanth        1044.612
Radley Whitehead    1043.892
Bert Faimon          594.49
Lolita Barkley       591.99
Denise Mulholland    590.69

Optimizing Star Queries

A typical query in the access layer is a join between the fact table and some number of dimension tables and is often referred to as a star query. In a star query each dimension table is joined to the fact table using a primary key to foreign key join. Normally the dimension tables do not join to each other.

Typically, in this kind of query all of the WHERE clause predicates are on the dimension tables and the fact table. Optimizing this type of query is very straight forward.

To optimize this query, do the following:

- Create a bitmap index on each of the foreign key columns in the fact table or tables

- Set the initialization parameter STAR_TRANSFORMATION_ENABLED to TRUE.

This enables the optimizer feature for star queries which is off by default for backward compatibility.

If your environment meets these two criteria, your star queries should use a powerful optimization technique that rewrites or transforms your SQL called star transformation. Star transformation executes the query in two phases:

1. Retrieves the necessary rows from the fact table (row set).

2. Joins this row set to the dimension tables.

The rows from the fact table are retrieved by using bitmap joins between the bitmap indexes on all of the foreign key columns. The end user never needs to know any of the details of STAR_TRANSFORMATION, as the optimizer automatically chooses STAR_TRANSFORMATION when it is appropriate.

Example 5-2 gives the step by step process to use STAR_TRANSFORMATION to optimize a star query.

Example 5-2 Star Transformation

A business question that could be asked against the star schema in Figure 3-1, "Star Schema Diagram" would be "What was the total number of umbrellas sold in Boston during the month of May 2008?"

1. The original query.

select SUM(quantity_sold) total_umbrellas_sold_in_Boston
From Sales s, Customers c, Products p, Times t
Where s.cust_id=cust_id
And s.prod_id = p.prod_id
And s.time_id=t.time_id
And c.cust_city='BOSTON'
And p.product='UMBRELLA'
And t.month='MAY'
And t.year=2012;

As you can see all of the where clause predicates are on the dimension tables and the fact table (Sales) is joined to each of the dimensions using their foreign key, primary key relationship.

2. Take the following actions:

1. Create a bitmap index on each of the foreign key columns in the fact table or tables.

2. Set the initialization parameter STAR_TRANSFORMATION_ENABLED to TRUE.

3. The rewritten query. Oracle rewrites and transfers the query to retrieve only the necessary rows from the fact table using bitmap indexes on the foreign key columns

select SUM(quantity_sold
From Sales
Where cust_id IN
(select c.cust_id From Customers c Where c.cust_city='BOSTON')
And s.prod_id IN
(select p.prod_id From Products p Where  p.product='UMBRELLA')
And s.time_id IN
(select t.time_id From Times(Where t.month='MAY' And t.year=2012);

By rewriting the query in this fashion you can now leverage the strengths of bitmap indexes. Bitmap indexes provide set based processing within the database, allowing you to use various fact methods for set operations such as AND, OR, MINUS, and COUNT. So, you use the bitmap index on time_id to identify the set of rows in the fact table corresponding to sales in May 2008. In the bitmap the set of rows are actually represented as a string of 1's and 0's. A similar bitmap is retrieved for the fact table rows corresponding to the sale of umbrellas and another is accessed for sales made in Boston. At this point there are three bitmaps, each representing a set of rows in the fact table that satisfy an individual dimension constraint. The three bitmaps are then combined using a bitmap AND operation and this newly created final bitmap is used to extract the rows from the fact table needed to evaluate the query.

4. Using the rewritten query, Oracle joins the rows from fact tables to the dimension tables.
The join back to the dimension tables is normally done using a hash join, but the Oracle Optimizer selects the most efficient join method depending on the size of the dimension tables.

The following figure shows the typical execution plan for a star query when STAR_TRANSFORMATION has kicked in. The execution plan may not look exactly as you expected. There is no join back to the customer table after the rows have been successfully retrieved from the Sales table. If you look closely at the select list, you can see that there is not anything actually selected from the Customers table so the optimizer knows not to bother joining back to that dimension table. You may also notice that for some queries even if STAR_TRANSFORMATION does kick in it may not use all of the bitmap indexes on the fact table. The optimizer decides how many of the bitmap indexes are required to retrieve the necessary rows from the fact table. If an additional bitmap index would not improve the selectivity, the optimizer does not use it. The only time you see the dimension table that corresponds to the excluded bitmap in the execution plan is during the second phase or the join back phase.

Troubleshooting Oracle Utilities Data Model Report Performance

Take the following actions to identify problems generating a report created using Oracle Business Intelligence Suite Enterprise Edition:

1. In the (Online) Oracle BI Administrator Tool, select Manage, then Security, then Users, and then oudm.
Ensure that the value for Logging level is 7.

2. Open the Oracle Utilities Data Model Repository, select Manage, and then Cache.

3. In the right-hand pane of the Cache Manager window, select all of the records, then right-click and select Purge.

4. Run the report or query that you want to track using the SQL log.

5. Open the query log file (NQQuery.log) under OracleBI\server\Log.
The last query SQL is the log of the report you have just run. If an error was returned in your last accessed report, there is an error at the end of this log.

The following examples illustrate how to use these error messages:

- Example 5-3, "Troubleshooting an Oracle Utilities Data Model Report"

- Example 5-4, "Troubleshooting a Report: A Table Does Not Exist"

- Example 5-5, "Troubleshooting a Report: When the Database is Not Connected"

Example 5-3 Troubleshooting an Oracle Utilities Data Model Report

Assume the log file contains the following error.

Query Status: Query Failed: [nQSError: 15018] Incorrectly defined logical table source (for fact table Customer Mining) does not contain mapping for [Customer.Address Location Type, Customer.Customer Segment Name, Customer.Party Name].

This error occurs when there is a problem in the Business layer in your Oracle Business Intelligence Suite Enterprise Edition repository.

In this case, you need to check the mapping for Customer.Addres Location Type, Customer.Customer Segment Name, and Customer.Party Name.

Example 5-4 Troubleshooting a Report: A Table Does Not Exist

Assume the log file contains the following error.

Query Status: Query Failed: [encloser: 17001] Oracle Error code: 942, message: ORA-00942: table or view does not exist.

This error occurs when the physical layer in your Oracle Business Intelligence Suite Enterprise Edition repository has the table which actually does not exist in the Database.

To find out which table has problem:

1. Copy the SQL query to database environment.

2. Execute the query.

The table which does not exist is marked out by the database client.

Example 5-5 Troubleshooting a Report: When the Database is Not Connected

Assume the log file contains the following error.

Error: Query Status: Query Failed: [nQSError: 17001] Oracle Error code: 12545, message: ORA-12545: connect failed because target host or object does not exist.

Meaning: This error occurs when the Database is not connected.

Action: Check connecting information in physical layer and ODBC connection to ensure that the repository is connecting to the correct database.

Writing As Is and As Was Queries

Two common query techniques are "as is" and "as was" queries:

- Characteristics of an As Is Query

- Characteristics of an As Was Query

- Examples: As Is and As Was Queries Against Oracle Utilities Data Model

Characteristics of an As Is Query

An As Is query has the following characteristics:

- The resulting report shows the data as it happened.

- The snowflake dimension tables are also joined using the surrogate key columns (that is the primary key and foreign key columns).

- The fact table is joined with the dimension tables (at leaf level) using the surrogate key column.

- Slowly-changing data in the dimensions are joined with their corresponding fact records and are presented individually.

- It is possible to add up the components if the different versions share similar characteristics.

Characteristics of an As Was Query

An As Was query (also known as point-in-time analysis) has the following characteristics:

- The resulting report shows the data that would result from freezing the dimensions and dimension hierarchy at a specific point in time.

- Each snowflake table is initially filtered by applying a point-in-time date filter which selects the records or versions which are valid as of the analysis date. This structure is called the point-in-time version of the snowflake.

- The filtered snowflake is joined with an unfiltered version of itself by using the natural key. All of the snowflake attributes are taken from the point-in-time version alias. The resulting structure is called the composite snowflake.

- A composite dimension is formed by joining the individual snowflakes on the surrogate key.

- The fact table is joined with the composite dimension table at the leaf level using the surrogate key column.

- The point-in-time version is super-imposed on all other possible SCD versions of the same business entity -- both backward and forward in time. Joining in this fashion gives the impression that the dimension is composed of only the specific point-in-time records.

- All of the fact components for various versions add up correctly due to the super-imposition of point-in-time attributes within the dimensions.

Examples: As Is and As Was Queries Against Oracle Utilities Data Model

Based on the "Data used for the examples", the following examples illustrate the characteristics of As Is and As Was queries:

- Example 5-6, "As Is Query for Tax Collection Split by Marital Status"

- Example 5-7, "As Was Queries for Tax Collection Split by Marital Status"

- Example 5-8, "As Is Query for Tax Collection Data Split by County"

- Example 5-9, "As Was Queries for Tax Collection Data Split by County"

Data used for the examples

Assume that your data warehouse has a Customer table, a County, and a TaxPaid fact table. As of January 1, 2012, these tables include the values shown:

Customer Table

Cust Id

Cust Cd

Cust Nm

Gender

M Status

County Id

County Cd

Country Nm

...

Eff Frm

Eff To

101

JoD

John Doe

Male

Single

5001

SV

Sunnyvale

...

1-Jan-12

31-Dec-99

102

JaD

Jane Doe

Female

Single

5001

SV

Sunnyvale

...

1-Jan-12

31-Dec-99

103

JiD

Jim Doe

Male

Married

5002

CU

Cupertino

...

1-Jan-12

31-Dec-99

County Table

County Id

County CD

County Nm

Population

...

Eff Frm

Eff To

5001

SV

Sunnyvale

Very High

...

1-Jan-12

31-Dec-99

5002

CU

Cupertino

High

...

1-Jan-12

31-Dec-99

TaxPaid Table

Cust Id

Day

Tax Type

Tax

101

1-Jan-12

Professional Tax

100

102

1-Jan-12

Professional Tax

100

103

1-Jan-12

Professional Tax

100

Assume that the following events occurred in January 2012:

- On January 20, 2012, Jane Doe marries.

- On Jan 29, 2012, John Doe moves from Sunnyvale to Cupertino.

Consequently, as shown, on February 1, 2012, the Customer and TaxPaid tables have new data while the values in the County table stay the same.

Customer table

Cust Id

Cust Cd

Cust Nm

Gender

M Status

County Id

County Cd

Country Nm

...

Eff Frm

Eff To

101

JoD

John Doe

Male

Single

5001

SV

Sunnyvale

...

1-Jan-12

29-Jan-12

102

JaD

Jane Doe

Female

Single

5001

SV

Sunnyvale

...

1-Jan-12

20-Jan-12

103

JiD

Jim Doe

Male

Married

5002

CU

Cupertino

...

1-Jan-12

31-Dec-99

104

JaD

Jane Doe

Female

Married

5001

SV

Sunnyvale

...

21-Jan-12

31-Dec-99

105

JoD

John Doe

Male

Single

5002

CD

Cupertino

...

30-Jan-12

31-Dec-99

County table

County Id

County CD

County Nm

Population

...

Eff Frm

Eff To

5001

SV

Sunnyvale

Very High

...

1-Jan-12

31-Dec-99

5002

CU

Cupertino

High

...

1-Jan-12

31-Dec-99

TaxPaid Table

Cust Id

Day

Tax Type

Tax

101

1-Jan-12

Professional Tax

100

102

1-Jan-12

Professional Tax

100

103

1-Jan-12

Professional Tax

100

105

1-Feb-12

Professional Tax

100

104

1-Feb-12

Professional Tax

100

103

1-Feb-12

Professional Tax

100

Example 5-6 As Is Query for Tax Collection Split by Marital Status

Assuming the "Data used for the examples", to show the tax collection data split by martial status, the following SQL statement that joins the TaxPaid fact table and the Customer dimension table on the cust_id surrogate key and the Customer and County snowflakes on the cnty_id surrogate key.

SELECT cust.cust_nm, cust.m_status, SUM(fct.tx)
FROM taxpaid fct, customer cust, county cnty
WHERE fct.cust_id = cust.cust_id
AND cust.cnty_id = cnt.cnt_id
GROUP BY cust.cust_nm, cust.m_status
ORDER BY 1,2,3;

The results of this query are shown. Note that there are two rows for Jane Doe; one row for a marital status of Married and another for a marital status of Single.

Cust Nm

M Status

Tax

Jane Doe

Married

100

Jane Doe

Single

100

Jim Doe

Married

200

John Doe

Single

200

Example 5-7 As Was Queries for Tax Collection Split by Marital Status

Assuming the "Data used for the examples", issue the following SQL statement to show the tax collection data split by marital status using an analysis date of January 15, 2012.

select
cust.cust_nm, cust.m_status, sum(fct.tax)
from
TaxPaid fct,
(
select
cust_act.cust_id, cust_pit.cust_cd, cust_pit.cust_nm,
cust_pit.m_status, cust_pit.gender,
cust_pit.cnty_id, cust_pit.cnty_cd, cust_pit.cnty_nm
from Customer cust_act
inner join (
select
cust_id, cust_cd, cust_nm,
m_status, gender,
cnty_id, cnty_cd, cnty_nm
from Customer cust_all
where to_date('15-JAN-2012', 'DD-MON-YYYY') between eff_from and eff_to
) cust_pit
on (cust_act.cust_cd = cust_pit.cust_cd)
) cust,
(
select
cnty_act.cnty_id, cnty_pit.cnty_cd, cnty_pit.cnty_nm
from County cnty_act
inner join (
select
cnty_id, cnty_cd, cnty_nm
from County cnty_all
where to_date('15-JAN-2012', 'DD-MON-YYYY') between eff_from and eff_to
) cnty_pit
on (cnty_act.cnty_cd = cnty_pit.cnty_cd)
) cnty
where fct.cust_id = cust.cust_id
and cust.cnty_id = cnty.cnty_id
GROUP BY cust.cust_nm, cust.m_status
order by 1,2,3;

The results of this query are shown. Since Jane Doe was single on January 15, 2012 (the analysis date), all tax for Jane Doe is accounted under her Single status.

Cust Nm

M Status

Tax

Jane Doe

Single

200

Jim Doe

Married

200

John Doe

Single

200

Assume instead that you issued the exact same query except that for the to_date phrase you specify 09-FEB-2012 rather than 15-JAN-2012. Since Jane Doe was married on February 9, 2012, then, as shown, all tax for Jane Doe would be accounted under her Married status.

Cust Nm

M Status

Tax

Jane Doe

Married

200

Jim Doe

Married

200

John Doe

Single

200

Example 5-8 As Is Query for Tax Collection Data Split by County

Assuming the "Data used for the examples", issue the following SQL statement to show the tax collection data split by county.

SELECT cust.cust_nm, cnty.cnty_nm, SUM(fct.tax)
FROM TaxPaid fct, customer cust, county cnty
WHERE fct.cust_id = cust.cust_id
AND cust.cnty_id = cnty.cnty_ID
GROUP BY cut.cust_nm, cnty.cnty_nm
ORDER BY 1,2,3;

The results of this query are shown. Note that since John Doe lived in two different counties, there are two rows of data for John Doe.

Cust Nm

County Nm

Tax

Jane Doe

Sunnyvale

200

Jim Doe

Cupertino

200

John Doe

Cupertino

100

John Doe

Sunnyvale

100

Example 5-9 As Was Queries for Tax Collection Data Split by County

Assuming the "Data used for the examples", issue the following SQL statement to show the tax collection data split by county using an analysis date of January 15, 2012.

select
cust.cust_nm, cnty.cnty_nm, sum(fct.tax)
from
TaxPaid fct,
(
select
cust_act.cust_id, cust_pit.cust_cd, cust_pit.cust_nm,
cust_pit.m_status, cust_pit.gender,
cust_pit.cnty_id, cust_pit.cnty_cd, cust_pit.cnty_nm
from Customer cust_act
inner join (
select
cust_id, cust_cd, cust_nm,
m_status, gender,
cnty_id, cnty_cd, cnty_nm
from Customer cust_all
where to_date('15-JAN-2012', 'DD-MON-YYYY') between eff_from and eff_to
) cust_pit
on (cust_act.cust_cd = cust_pit.cust_cd
) cust,
(
select
cnty_act.cnty_id, cnty_pit.cnty_cd, cnty_pit.cnty_nm
from County cnty_act
inner join (
select
cnty_id, cnty_cd, cnty_nm
from County cnty_all
where to_date('15-JAN-2012', 'DD-MON-YYYY') between eff_from and eff_to
) cnty_pit
on (cnty_act.cnty_cd = cnty_pit.cnty_cd)
) cnty
where fct.cust_id = cust.cust_id
and cust.cnty_id = cnty.cnty_id
GROUP BY cust.cust_nm, cnty.cnty_nm
order by 1,2,3;

The results of this query are shown. Note that since John Doe was in Sunnyvale as of the analysis date of January 15, 2012, all tax for John Doe is accounted for under the Sunnyvale county.

Cust Nm

County Nm

Tax

Jane Doe

Sunnyvale

200

Jim Doe

Cupertino

200

John Doe

Sunnyvale

200

Assume instead that you issued the exact same query except that for the to_date phrase you specify 09-FEB-2012 rather than 15-JAN-2012. Since John Doe lived in Cupertino on February 9, 2012, then, as shown, all tax for John Doe would be accounted under Cupertino.

Cust Nm

County Nm

Tax

Jane Doe

Sunnyvale

200

Jim Doe

Cupertino

200

John Doe

Cupertino

200

Tutorial: Creating a New Oracle Utilities Data Model Dashboard

This tutorial explains how to create a dashboard based on the Oracle Utilities Data Model webcat included with the sample Oracle Business Intelligence Suite Enterprise Edition reports delivered with Oracle Utilities Data Model.

See:
Oracle Utilities Data Model Installation Guide for more information on installing the sample reports and deploying the Oracle Utilities Data Model RPD and webcat on the Business Intelligence Suite Enterprise Edition instance.

To create a dashboard, take the following steps:

1. In the browser, open the login page at http://servername:7001/analytics where servername is the server on which the webcat is installed.

2. Login with username of oudm, and provide the password.
Then, click newDashboard to create an Oracle Business Intelligence Suite Enterprise Edition dashboard.

3. Input name and description, save it to the Sandbox folder. Click OK.

4. In the Catalog view, expand the OUDM folder and KPI Reports folder. You can see Top N Customers by Usage.

5. Drag the Top N Customers by Usage report into the right panel:

6. To change the page name:

1. Select the Dashboard.

2. In Dashboard Properties window, click Change Name.

3. Change the name to "Top N Customers", then click OK.

7. Click Save on the top of the dashboard. Now you have a new dashboard.

Oracle by Example:
For more information on creating dashboards see the "Creating Analyses and Dashboards 11g" OBE tutorial.
To access the tutorial, open the Oracle Learning Library in your browser by following the instructions in "Oracle Technology Network"; and, then, search for the tutorial by name.

Tutorial: Creating a New Oracle Utilities Data Model Report

This tutorial explains how to create a report based on the Oracle Utilities Data Model webcat included with the sample Oracle Business Intelligence Suite Enterprise Edition reports delivered with Oracle Utilities Data Model.

See:
Oracle Utilities Data Model Installation Guide for more information on installing the sample reports and deploying the Oracle Utilities Data Model RPD and webcat on the Business Intelligence Suite Enterprise Edition instance.

In this example, assume that you want to create a report named "Monthly Total Usage".

To create a this new report, take the following steps:

1. In the browser, open the login page at http://servername:7001/analytics where servername is the server on which the webcat is installed.

2. Login with username of oudm, and provide the password.
Then, click newAnalysis to create an Oracle Business Intelligence Suite Enterprise Edition report.

3. Select Subject Area, then select OUDM to create a relational report.

4. Drag and put the dimension and fact columns into the Select Columns panel.

5. Select the Results tab to view the report. Click New View to add a chart into the report:

6. Click Save to save this report into one of the desired folder.

Oracle by Example:
For more information on creating a report, see the "Creating Analyses and Dashboards 11g" OBE tutorial.
To access the tutorial, open the Oracle Learning Library in your browser by following the instructions in "Oracle Technology Network"; and, then, search for the tutorial by name.

6 Metadata Collection and Reports

This chapter includes the following sections:

- Overview of Managing Metadata for Oracle Utilities Data Model

- Browsing Metadata Reports and Dashboard

- Collecting and Populating Metadata

Overview of Managing Metadata for Oracle Utilities Data Model

Metadata is any data about data and, as such, is an important aspect of the data warehouse environment. Metadata allows the end user and the business analyst to navigate through the possibilities at a higher business object level.

Metadata management is a comprehensive, ongoing process of overseeing and actively managing metadata in a central environment which helps an enterprise to identify how data is constructed, what data exists, and what the data means. It is particularly helpful to have good metadata management when customizing Oracle Utilities Data Model so that you can do impact analysis to ensure that changes do not adversely impact data integrity anywhere in your data warehouse.

- Metadata Categories and Standards

- Working with a Metadata Repository

Metadata Categories and Standards

Metadata is organized into three major categories:

- Business metadata describes the meaning of data in a business sense. The business interpretation of data elements in the data warehouse is based on the actual table and column names in the database. Business metadata gathers this mapping information, business definitions, and rules information.

- Technical metadata represents the technical aspects of data, including attributes such as data types, lengths, lineage, results from data profiling, and so on.

- Process execution metadata presents statistics on the results of running the ETL process itself, including measures such as rows loaded successfully, rows rejected, amount of time to load, and so on.

Since metadata is so important in information management, many organizations attempt to standardize metadata at various levels, such as:

- Metadata Encoding and Transmission Standard (METS). A standard for encoding descriptive, administrative, and structural metadata regarding objects within a digital library.

- American National Standards Institute (ANSI). The organization that coordinates the U.S. voluntary standardization and conformity-assessment systems.

- International Organization for Standardization (ISO). The body that establishes, develops, and promotes standards for international exchange.

- Common Warehouse Metamodel (CWM). A specification, released and owned by the Object Management Group, for modeling metadata for relational, non-relational, multi-dimensional, and most other objects found in a data warehousing environment.

When you implement your metadata management solution, reference your data warehouse infrastructure environment and make the decision which standard to follow.

Working with a Metadata Repository

You manage metadata using a Metadata Repository. At the highest level, a Metadata Repository includes three layers of information. The layers are defined in the following order:

1. Physical layer: this metadata layer identifies the source data.

2. Business Model and Mapping layer: this metadata layer organizes the physical layer into logical categories and records the appropriate metadata for access to the source data.

3. Presentation layer: this metadata layer exposes the business model entities for end-user access.

The first step in creating a Metadata Repository is to scope your metadata management needs by:

- Identifying the metadata consumers. Typically, there are business consumers and technical consumers.

- Determine the business and technical metadata requirements.

- Aligning metadata requirements to specific data elements and logical data flows.

Then:

- Decide how important each part is.

- Assign responsibility to someone for each piece.

- Decide what constitutes a consistent and working set of metadata

- Where to store, backup, and recover the metadata.

- Ensure that each piece of metadata is available only to those people who need it.

- Quality-assure the metadata and ensure that it is complete and up to date.

- Identify the Metadata Repository to use and how to control that repository from one place

After creating the metadata definitions, review your data architecture to ensure you can acquire, integrate, and maintain the metadata.

As the data keeps on changing in your data warehouse day by day, update the Metadata Repository. When you want to change business rules, definitions, formulas or process (especially when customizing the Oracle Utilities Data Model), your first step is to survey the metadata and do an impact analysis to list all of the attributes in the data warehouse environment that would be affected by a proposed change.

Browsing Metadata Reports and Dashboard

To customize the Oracle Utilities Data Model model, you must understand the dependencies among Oracle Utilities Data Model components, especially how the report KPIs are mapped to the physical tables and columns. Oracle Utilities Data Model provides a tool, the OUDM Metadata browser that helps you discover these dependencies. When you install Oracle Utilities Data Model with its sample reports, the metadata browser is delivered as a sample Dashboard in the webcat.

See:
Oracle Utilities Data Model Installation Guide for more information on installing the sample reports and deploying the Oracle Utilities Data Model RPD and webcat on the Business Intelligence Suite Enterprise Edition instance.

There are four tabs (reports) in the Oracle Utilities Data Model Metadata browser. To browse the metadata repository:

1. In the browser, open the login page at http://servername:9704/analytics where servername is the server on which the webcat is installed.

2. Login with username of oudm, and provide the password.

3. Select the Metadata Browser dashboard.

4. Use the tabs in the Metadata browser to explore the metadata.

- Measure-Entity tab
On the Measure-Entity tab you can see the business areas (relational, OLAP, mining), the measures description, corresponding formula, responsible entities, and attributes for the measure.

- Entity-Measure tab
Using the Entity-Measure tab, you can discover the mappings between entities, attributes, supported measures, and calculations of the measures. You can discover information about particular entities and attributes.

- Program-Table tab
Using the Program-Table tab you can browse for information on the intra-ETL mappings and report information. Take the following steps:

- Table-Program tab
By default when you go to the Table-Program tab you see all of the tables used for all the reports.

To discover what reports use a particular table, you must move a particular table from the right pane to the left (Selected) pane.

Using the Measure-Entity Tab Business Areas and Measures Attributes and Entities

The Measure-Entity tab provides information on the measure descriptions, computational formulas with physical columns, physical tables, and corresponding entities by Business Area.

To browse the Measure-Entity data, select the business area and measure description that you are interested in.

Using the Entity-Measure Tab Entity to Attribute Measures

The Entity-Measure tab displays the measures supported by the entities and how they are calculated. You can discover information about particular entities and attributes.

To view the Entity-Measure tab perform the following steps to learn more about an entity:

1. Select the entity.

2. Click GO.

Using the Program-Table Tab

The Program-Table tab displays the input and output tables used in the selected programs.

To use the Program-Table tab, perform the following steps to learn more about intra-ETL mappings:

1. Select the program type (that is, intra-ETL or report) and program name for showing particular report or intra-ETL information.

2. Select GO.

Using the Table-Program Tab

The Table-Program tab lists the Programs used by a given table and whether that table is an input or output, or both, of that program. To discover what reports use a particular table, move a particular table from the right pane to the left (Selected) pane.

To see the reports that use a particular table, perform the following steps:

1. In the right pane of the Table-Program tab, select the table.

2. Move the table to the Selected list on the left by clicking on < (left arrow), and click OK.

3. Select GO.
The reports for the selected table are displayed.

Collecting and Populating Metadata

The Oracle Utilities Data Model metadata browser generation packages generate and update the Oracle Utilities Data Model metadata. The metadata generation package contains four main tables and several staging tables and views. The metadata generation tables are:

- MD_ENTY

- MD_PRG

- MD_KPI

- MD_REF_ENTY_KPI

Use the following steps to collect and populate the metadata.

1. Collect LDM Metadata:
Extract the Logical Data Model repository metadata from Oracle SQL Developer Data Modeler (OSDM) into a database schema. Use manual steps to generate Logical Data Model repository tables in the database with Oracle SQL Developer Data Modeler.

1. Start Oracle SQL Developer Data Modeler

2. Open Logical Data Model

3. Select File.

4. Select Export.

5. Select To Reporting Schema.

2. Collect Sample Dashboard Metadata:
Extract the BIEE dashboard metadata from webcat to csv file.

Using OBIEE catalog manager open the SQL Developer sample report webcat:

Tools -> create Report -> Select type to report on -> select dashboard

Select columns one by one as shown in the md_dashboard.ldr specified in the meta_data folder, then save as a csv format file, md_dashboard.csv.

Put this file in the meta_data folder.

Column Sequence:

1. Name

2. Description

3. Path

4. Folder

5. Analysis Path

6. Analysis Name

7. Analysis Description

8. Dashboard Page Description

9. Dashboard Page Name

10. Dashboard Page Path

11. Owner

3. Collect Sample Report Metadata:
Extract BIEE report metadata from webcat to csv file. Use OBIEE catalog manager to open Oracle Utilities Data Model sample report webcat.

- Tools -> create Report -> Select type to report on -> select Analysis -> select columns one by one as shown in the md_dashboard.ldr specified in the meta_data folder.

- Save the file as csv format, md_dashboard.csv. Put the file under meta_data folder

Column Sequence:

1. NAME

2. DESCRIPTION

3. TABLE_NAME

4. COLUMN_NAME

5. FOLDER

6. PATH

7. SUBJECT_AREA

8. FORMULA

4. Collect Sample RPD Metadata:
Extract BIEE RPD metadata from RPD to csv file. Use Administrator Tool to open Oracle Utilities Data Model sample report RPD:

- Tools -> Utilities -> Repository Documentation -> Execute -> select location -> set xls file name as md_rpd.

- Save as csv format md_rpd.csv and put under meta_data folder.

5. Load Naming Convention Information:
Load Oracle Utilities Data Model Physical Data Model naming convention information from csv into a staging table. Use sqlloader to load data from name_conversion.csv into MD_NAME_CONVERSION table. The sqlloader format file: Name_conversion.ldr

Name_conversion.ldr:
OPTIONS (SKIP=1)
LOAD DATA
INFILE      'name_conversion.csv'
BADFILE     'name_conversion.csv.bad'
DISCARDFILE 'name_conversion.csv.dsc'
truncate
INTO TABLE MD_NAME_CONVERSION
FIELDS TERMINATED BY ',' OPTIONALLY ENCLOSED BY '"'
TRAILING NULLCOLS
(
ABBREVIATION    ,
FULL_NAME
)

6. Load Sample Dashboard Metadata:
Load sample dashboard metadata from csv into a staging table. Use sqlloader to load data from md_dashboard.csv into MD_DASHBOARD table. The sqlloader format file: md_dashboard.ldr.

Md_dashboard.ldr:

OPTIONS (SKIP=1)
LOAD DATA
INFILE      'md_dashboard.csv'
BADFILE     'md_dashboard.csv.bad'
DISCARDFILE 'md_dashboard.csv.dsc'
truncate
INTO TABLE MD_DASHBOARD
FIELDS TERMINATED BY ',' OPTIONALLY ENCLOSED BY '"'
TRAILING NULLCOLS
(
NAME char(2000),
DESCRIPTION char(2000),
PATH char(2000),
FOLDER char(2000),
ANALYSIS_PATH char(2000),
ANALYSIS_NAME char(2000),
ANALYSIS_DESCRIPTION char(2000),
DASHBOARD_PAGE_DESCRIPTION char(2000),
DASHBOARD_PAGE_NAME char(2000),
DASHBOARD_PAGE_PATH char(2000),
OWNER char(2000)
)

7. Load Sample Report Metadata
Load sample report metadata from csv into a staging table. Use sqlloader to load data from md_report.csv into MD_REPORT table. The sqlloader format file: md_report.ldr.

Md_dashboard.ldr:

OPTIONS (SKIP=1)
LOAD DATA
INFILE      'md_dashboard.csv'
BADFILE     'md_dashboard.csv.bad'
DISCARDFILE 'md_dashboard.csv.dsc'
truncate
INTO TABLE MD_DASHBOARD
FIELDS TERMINATED BY ',' OPTIONALLY ENCLOSED BY '"'
TRAILING NULLCOLS
(
NAME char(2000),
DESCRIPTION char(2000),
PATH char(2000),
FOLDER char(2000),
ANALYSIS_PATH char(2000),
ANALYSIS_NAME char(2000),
ANALYSIS_DESCRIPTION char(2000),
DASHBOARD_PAGE_DESCRIPTION char(2000),
DASHBOARD_PAGE_NAME char(2000),
DASHBOARD_PAGE_PATH char(2000),
OWNER char(2000)
)

8. Load Sample RPD Metadata:
Load sample RPD metadata from csv into a staging table.

Note:
If the OLAP part of the RPD is populated by the BIEE native OLAP import. Then the metadata of this part will not be shown in md_rpd.csv. You need to manually populate this part of metadata from the RPD.

Use sqlloader to load data from md_rpd.csv into MD_RPD table. The sqlloader format file: md_rpd.ldr.

Md_rpd.ldr:

OPTIONS (SKIP=0)
LOAD DATA
INFILE      'md_rpd.csv'
BADFILE     'md_rpd.csv.bad'
DISCARDFILE 'md_rpd.csv.dsc'
truncate
INTO TABLE MD_RPD
FIELDS TERMINATED BY ',' OPTIONALLY ENCLOSED BY '"'
TRAILING NULLCOLS
(
SUBJECT_AREA
,PRESENTATION_TABLE
,PRESENTATION_COLUMN char(500)
,DESC_PRESENTATION_COLUMN
,BUSINESS_MODEL
,DERIVED_LOGICAL_TABLE
,DERIVED_LOGICAL_COLUMN
,DESC_DERIVED_LOGICAL_COLUMN
,EXPRESSION char(1000)
,LOGICAL_TABLE
,LOGICAL_COLUMN
,DESC_LOGICAL_COLUMN
,LOGICAL_TABLE_SOURCE
,EXPRESSION_1 char(1000)
,INITIALIZATION_BLOCK
,VARIABLE
,DATABASE
,PHYSICAL_CATALOG
,PHYSICAL_SCHEMA
,PHYSICAL_TABLE
,ALIAS
,PHYSICAL_COLUMN
,DESC_PHYSICAL_COLUMN
)

9. Load LDM/PDM Metadata (Table MD_ENTY):
Load LDM/PDM mapping and related information into table MD_ENTY. For information on this step, see "Load LDM/PDM Metadata (Table MD_ENTY)".

10. Load Program (Intra-ETL) Metadata (Table MD_PRG):
Load Intra-ETL program input/output and related information into table MD_PRG.

For information on this step, see "Load Program (Intra-ETL) Metadata (Table MD_PRG)"

11. Load Reports and KPI Metadata (Table - MD_KPI and MD_REF_ENTY_KPI)
Load sample report metadata into MD_KPI and load report/PDM/LDM mapping related information into table MD_REF_ENTY_KPI.

For information on this step see "Load Reports and KPI Metadata (Table MD_KPI and MD_REF_ENTY_KPI):".

Load LDM/PDM Metadata (Table MD_ENTY)

If you want to get the mapping between a business area and an entity, you have to manually populate this information. You can only get this information from the metadata report for those entities which are used in the report, for those entities which are not used in report, you have to manually map them to the correct business area.

Source Tables Required

Source Table Name

Description

DMRS_ATTRIBUTES

Containing attributes of the particular entity

DMRS_ENTITIES

Containing entity name with unique id

MD_NAME_CONVERSION

Containing full name and abbreviation of the distinct word used in the LDM

Staging Tables/Views

Staging Table/View Name

Description

MD_OIDM_ATTR_COL_NAME_MAP

Used to store abbreviate the column names based on the standard abbreviation used in the project.

MD_DM_ALL_ENT_ATTR

Used to generate and keep the entity description.

Loading MD_ENTY (MD_ENTY_POP.SQL)

GIVE_ABBRV

Type: Function

This database function GIVE_ABBRV provides the abbreviation for a named token from the table MD_NAME_CONVERSION.

Source Table

MD_NAME_CONVERSION

Columns: ABBREVIATION

Target

Table: MD_OIDM_ATTR_COL_NAME_MAP

Columns: column_name_abbr

MD_DM_ALL_ENT_ATTR

Type: View

This database view provides the description of each entity.

Source Table

Target View

DMRS_ENTITIES

MD_DM_ALL_ENT_ATTR

PL/SQL Program to Update Column Name

Type: PL/SQL Program

This program updates the column name based on the result of function GIVE_ABBRV.

Source Tables

Target Table

MD_OIDM_ATTR_COL_NAME_MAP
DMRS_ATTRIBUTES

MD_OIDM_ATTR_COL_NAME_MAP
Column: column_name_abbr

PL/SQL program to insert initial data into MD_OIDM_ATTR_COL_NAM

Type: PL/SQL Program

Provides initial loading for table MD_OIDM_ATTR_COL_NAME_MAP

Source Tables

Target Table

MD_DM_ALL_ENT_ATTR
DMRS_ENTITIES

MD_OIDM_ATTR_COL_NAME_MAP

PL/SQL program to load data into MD_ENTY

Type: PL/SQL Program

Loads data into MD_ENTY from all the staging tables.

Source Table

Target Table

MD_OIDM_ATTR_COL_NAME_MAP

MD_ENTY

Load Program (Intra-ETL) Metadata (Table MD_PRG)

Source Tables Required

Source Table Name

Description

USER_DEPENDENCIES

This database view describes dependencies between procedures, packages, functions, package bodies, and triggers owned by the current user, including dependencies on views created without any database links.

MD_RPD_RPT

This table contains the sample report related information.

Staging Tables/Views

Staging Table/View Name

Description

MD_INTRA_ETL

Used to generate and keep the relational/OLAP ETL program metadata information.

MD_MINING

Used to generate and keep the data mining ETL program metadata information.

Loading MD_PRG (MD_PRG_POP.SQL, MD_MIN_PRG_POP.SQL)

Program: MD_INTRA_ETL

Type: View

This view extracts information for relational and OLAP Intra-ETL packages. The structure is the same as MD_PRG.

Source View

Target View

USER_DEPENDENCIES

MD_INTRA_ETL

Program: MD_MINING

Type: View

This view extracts information for the data mining Intra-ETL packages. The structure of the view same as MD_PRG.

Source View

Target View

USER_DEPENDENCIES

MD_MINING

Program: PL/SQL program to load ETL mapping data into MD_PRG.

Type: PL/SQL Program

Load ETL program data into MD_PRG from all the staging views

Source Views

Target Table

MD_INTRA_ETL
MD_MINING

MD_PRG

Program: PL/SQL program insert report data into MD_PRG

Type: PL/SQL Program

Load report data into MD_PRG from report staging table.

Source Table

Target Table

MD_RPD_RPT

MD_PRG

Load Reports and KPI Metadata (Table MD_KPI and MD_REF_ENTY_KPI):

Source Tables Required

Source Table Name

Description

MD_RPD

This tables stores all the RPD metadata information, it is directly loaded from md_rpd.csv

MD_REPORT

This tables stores all the report (analysis) metadata information, it is directly loaded from md_report.csv

MD_DASHBOARD

This tables stores all the sample report dashboard metadata information, it's directly loaded from md_dashboard.csv

Staging Tables/Views

Staging Table/View Name

Description

MD_RPD_CALC_PHY

Stores the missing physical tables and columns for derived measures. Wrote a query to find out missing Physical tables and columns for derived measures.

MD_REPORT1

MD_REPORT1 has the same structure of MD_RPT, it is used to store comma separated tables and columns to the new row, by that it can directly join with physical tables and columns from MD_RPD_CALC_PHY.

MD_RPT_DASH

Contains all mappings information between RPD and reports.

MD_RPD_RPT_DASH

Stores all the mappings information of Report, RPD and Dashboard.

Loading MD_KPI and MD_REF_ENTY_KPI (SAMPLE_REP_POP.SQL)

Program: PL/SQL program Insert non calculated columns Data Into MD_RPD_CALC_PHY

Type: PL/SQL Program

This program extracts those base KPIs or non calculated column information and inserts into MD_RPD_CALC_PHY.

Source Table

Target Table

MD_RPD

MD_RPD_CALC_PHY

Program: PROCEDURE Proc_DelmValuePopulate2

Type: Procedure

This procedure loads comma separated data to new row of the MD_REPORT1 table.

Source Table

Target Table

MD_REPORT

MD_REPORT1

Program: PL/SQL program to create and perform initial load of data into MD_RPD_RPT

Type: PL/SQL Program

This program creates and performs initial load of data for the table MD_RPD_RPT.

Source Tables

Target Table

MD_RPD_CALC_PHY
MD_REPORT1

MD_RPD_RPT

Program: PL/SQL program to create and initial load data into MD_RPD_RPT_DASH.

Type: PL/SQL Program

This program creates and performs initial load of data for table MD_RPD_RPT_DASH.

Source Tables

Target Table

MD_RPD_CALC_PHY
MD_RPT_DASH

MD_RPD_RPT_DASH

MD_RPD_RPT_DASH

Program: PL/SQL program to create and initial load data into MD_RPD_RPT.

Type: PL/SQL Program

This program creates performs initial load of data for table MD_RPD_RPT.

Source Tables

Target Table

MD_RPD_CALC_PHY
MD_REPORT1

MD_RPD_RPT

Program: MD_DRVD_KP

Type: View

This view extracts and keeps the information for all the calculated KPIs.

Source Table

Target Table

MD_RPD_RPT_DASH

MD_DRVD_KPI

Program: PL/SQL program to create and performs initial load of data into MD_KPI.

Type: PL/SQL Program

This program creates and performs initial load of data for table MD_KPI.

Source Table

Target Table

MD_RPD_RPT_DASH

MD_KPI

Program: PL/SQL program to create and initial load data into MD_REF_ENTY_KPI.

Type: PL/SQL Program

This program creates and performs the initial load of data for table MD_REF_ENTY_KPI.

Source Table

Target Table

MD_RPD_RPT_DASHI

MD_REF_ENTY_KPI

A Sizing and Configuring an Oracle Utilities Data Model Warehouse

This appendix provides information about sizing and configuring an Oracle Utilities Data Model warehouse. It contains the following topics:

- Sizing an Oracle Utilities Data Model Warehouse

- Configuring a Balanced System for Oracle Utilities Data Model

Sizing an Oracle Utilities Data Model Warehouse

Businesses now demand more information sooner and are delivering analytics from their Enterprise Data Warehouse (EDW) to an ever-widening set of users and applications. In order to keep up with this increase in demand the EDW must now be near real-time and be highly available. Regardless of the design or implementation of a data warehouse the initial key to good performance lies in the hardware configuration used. This has never been more evident than with the recent increase in the number of data warehouse appliances in the market.

But how do you go about sizing such a system? You must first understand how much throughput capacity is required for your system and how much throughput each individual CPU or core in your configuration can drive, thus the number one task is to calculate the database space requirement in your data warehouse.

There are two data volume estimate resources in a data warehouse environment:

- The estimated raw data extract from source systems. This estimate affects the ETL system configuration and the stage layer database space in data warehouse system. Because this value is determined by your unique OLTP system, you must calculate this information yourself.

- The space needed for data stored to support the objects defined in the default Oracle Utilities Data Model schema. This appendix provides information you can use to make this calculation.

Calculation Factors When Making a Data Volume Calculation for an Oracle Utilities Data Model Warehouse

Consider the following calculation factors when making a data volume calculation:

- Calculates data unit volume within different type:

- Reference and lookup tables data. Assume this data is permanently stored.

- Base tables data (transaction data). Assume that this data is stored within its life cycle.

- Star schema (derived and summary). Assume that this data is stored within its life cycle.

- Calculate each type of data retention.

- Define how many months or years of each type of tables to retain.

- Calculate data growth.

- Assume that annual growth rate: applies to both transaction and reference data and data in the star schema.

- Assume that annual change rate applies only to reference data.

- Calculate Staging Area data requirements, if proposed.

Tip:
Multiply ETL volume by day by number of days held for problem resolution and re-run of transform with new extract from source systems.

- Calculate data volume for indexes, temporary tables, and transaction logs.

- Calculate the space requirement for business intelligence tools, such as cubes, and data mining.

- Consider the redo log and Oracle ASM space requirement.

- Consider the RAID architecture [RAID 1, 0+1, 5]

- Consider the backup strategy.

- Consider the compress factor if applied.

- Consider the OS and file system disk space requirements.

Formula to Determine Minimum Disk Space Requirements for an Oracle Utilities Data Model Warehouse

Use the following formula, based on the factors outlined in "Calculation Factors When Making a Data Volume Calculation for an Oracle Utilities Data Model Warehouse", to determine the minimum disk space requirements for an Oracle Utilities Data Model warehouse.Foot 1

Disk Space Minimum Requirements = Raw data size * Database space factor * (1+GrthperY)nY*OS and File system factor * Compress Factor * Storage Redundant factor

where:

- Raw data size = (reference and lookup data per year + base/transaction data per year + derived and summary data per year +staging data +other data(OLAP/Data Mining))

- Database space factor = Indexes + Temporary Tables + Logs]

- GrthperY = growth rate per year

- OS and File system factor is the install and configuration and maintain space for OS and DB

- Redundant factor= ASM disk space and RAID factor. [ RAID 1=2, RAID 5=1.25 or 1.33 ]

- Compress factor depends how you apply the compress function. If you are executing on an Exadata Database machine, it has a huge savings in disk space by using compression.

Configuring a Balanced System for Oracle Utilities Data Model

Many data warehouse operations are based upon large table scans and other I/O-intensive operations, which perform vast quantities of random I/Os. In order to achieve optimal performance the hardware configuration must be sized end to end to sustain this level of throughput. This type of hardware configuration is called a balanced system. In a balanced system all components - from the CPU to the disks - are orchestrated to work together to guarantee the maximum possible I/O throughput. I/O performance is always a key consideration for data warehouse designers and administrators. The typical workload in a data warehouse is especially I/O intensive, with operations such as large data loads and index builds, creation of materialized views, and queries over large volumes of data. Design the underlying I/O system for a data warehouse to meet these heavy requirements.

To create a balanced system, answer the following questions:

- How many CPUs are required? What speed is required?

- What amount of memory is required? Data warehouse do not have the same memory requirements as mission-critical OLTP applications?

- How many I/O bandwidth components are required? What is the desired I/O speed?

Each component must be able to provide sufficient I/O bandwidth to ensure a well-balanced I/O system.

The following topics provide more information about configuring a balanced system for Oracle Utilities Data Model:

- Maintaining High Throughput in an Oracle Utilities Data Model Warehouse

- Configuring I/O in an Oracle Utilities Data Model for Bandwidth not Capacity

- Planning for Growth of Your Oracle Utilities Data Model

- Testing the I/O System Before Building the Warehouse

- Balanced Hardware Configuration Guidelines for Oracle Utilities Data Model

Maintaining High Throughput in an Oracle Utilities Data Model Warehouse

The hardware configuration and data throughput requirements for a data warehouse are unique mainly because of the sheer size and volume of data. Before you begin sizing the hardware configuration for your data warehouse, estimate the highest throughput requirement to determine whether current or proposed hardware configuration can deliver the necessary performance. When estimating throughput, use the following criteria:

- The amount of data accessed by queries during peak time, and the acceptable response time

- The amount of data that is loaded within a window of time

Configuring I/O in an Oracle Utilities Data Model for Bandwidth not Capacity

Based on the data volume calculated and the highest throughput requirement, you can estimate the I/O throughput along with back-end ETL process and front end business intelligence applications by time unit. Typically, a value of approximately 200 MB per second I/O throughput per core is a good planning number for designing a balanced system. All subsequent critical components on the I/O path - the Host Bus Adapters, fiber channel connections, the switch, the controller, and the disks - have to be sized appropriately.

When running a data warehouse on an Oracle Real Application Cluster (Oracle RAC) it is just as important to size the cluster interconnect with the same care and caution you would use for the I/O subsystem throughput.

When configuring the storage subsystem for a data warehouse, it should be simple, efficient, highly available and very scalable. An easy way to achieve this is to apply the S.A.M.E. methodology (Stripe and Mirror Everything). S.A.M.E. can be implemented at the hardware level or by using Oracle ASM (Automatic Storage Management) or by using a combination of both. There are many variables in sizing the I/O systems, but one basic rule of thumb is that the data warehouse system has multiple disks for each CPU (at least two disks for each CPU at a bare minimum) to achieve optimal performance.

Planning for Growth of Your Oracle Utilities Data Model

A data warehouse designer plans for future growth of a data warehouse. There are several approaches to handling the growth in a system, and the key consideration is to be able to grow the I/O system without compromising on the I/O bandwidth. You cannot, for example, add four disks to an existing system of 20 disks, and grow the database by adding a new tablespace striped across only the four new disks. A better solution would be to add new tablespaces striped across all 24 disks, and over time also convert the existing tablespaces striped across 20 disks to be striped across all 24 disks.

Testing the I/O System Before Building the Warehouse

When creating a data warehouse on a new system, test the I/O bandwidth before creating all of the database data files to validate that the expected I/O levels are being achieved. On most operating systems, you can perform the test using simple scripts to measure the performance of reading and writing large test files.

Balanced Hardware Configuration Guidelines for Oracle Utilities Data Model

You can reference the follow tips for a balanced hardware configuration:

- Total throughput = #cores X 100-200MB (depends on the chip set)

- Total host bus adapter (HBA) throughput = Total core throughput

Note:
If total core throughput is 1.6 GB, you need four 4 Gbit HBAs.

- Use one disk controller per HBA port (throughput capacity must be equal).

- Switches must have the capacity as HBAs and disk controllers.

- Use a maximum of ten physical disk per controller (that is, use smaller drives: 146 or 300 GB).

- Use a minimum of 4 GB of memory per core (8 GB if using compress).

- Interconnect bandwidth equals I/O bandwidth (InfiniBand).

Oracle now provides the Oracle Database Machine, Exadata which combines industry-standard hardware from Oracle, Oracle Database 11g Release 2, and Oracle Exadata Storage Server Software to create a faster, more versatile database machine. It's a completely scalable and fault tolerant package for all data management, especially for data warehousing.

Oracle also has a series of Optimized Warehouse Reference configurations that help customers take the risk out of designing and deploying Oracle data warehouses. Using extensive field experience and technical knowledge, Oracle and its hardware partners have developed a choice of data warehouse reference configurations that can support various sizes, user populations and workloads. These configurations are fast, reliable and can easily scale from 500 GB to over 100 TB on single and clustered servers to support tens to thousands of users.

Footnote Legend

Footnote 1: Carefully review whether these factors apply in your environment. These factors may not apply or may change in your environment, especially when using pretuned Exadata hardware.)

Index
A  C  D  E  F  H  I  J  K  M  N  O  P  Q  R  S  T

A

access layer, 2.1

customizing, 3.1

Oracle Utilities Data Model, 2.1

aggregate tables

in Oracle Utilities Data Model, 3.3

As Is reports, 5.6.1 As Was reports, 5.6.1

C

compression

in Oracle Utilities Data Model, 2.4.2, 2.4.2.1, 2.4.2.1.1

materialized views, 3.5.4

configuring Oracle Utilities Data Model warehouse, A.2 conventions

when customizing physical model, 2.2.2

cubes

adding materialized view capabilities to, 3.4.3.2

changing the dimensions of, 3.4.6

changing the measures of, 3.4.6

customizing, 3.4.4

data maintenance methods, 3.4.9

forecast, 3.4.7

in Oracle Utilities Data Model, 3.4.4, 3.4.5

partitioning, 3.4.8

customizing

cubes, 3.4.4

Oracle Utilities Data Model, 1.2

physical data model, 2

D

dashboards, Oracle Utilities Data Model, 5.2, 5.7

data governance committee, responsibilities of, 1.3.2

data mining models

customizing, 3.2.2

derived tables

in Oracle Utilities Data Model, 3.2

dimensional components, Oracle Utilities Data Model, 3.4

E

ETL for Oracle Utilities Data Model, 4.1

F

fit-gap analysis for Oracle Utilities Data Model, 1.4

forecast cube in Oracle Utilities Data Model, 3.4.7

foundation layer

defined, 2.1

Oracle Utilities Data Model, 2.1

foundation layer of Oracle Utilities Data Model

common change scenarios, 2.3.1

H

hybrid columnar compression

and Oracle Utilities Data Model, 2.4.2.1.3

I

implementers of Oracle Utilities Data Model

prerequisite knowledge, 1.3.1

implementing

Oracle Utilities Data Model, 1.2

indexes

in Oracle Utilities Data Model, 2.4.6

materialized views, 3.5.2

partitioning, 2.4.6

integrity constraints

in Oracle Utilities Data Model, 2.4.5

intra-ETL

Oracle Utilities Data Model, 4.1

J

join performance, improving, 2.4.7.3

K

keys, surrogate

in Oracle Utilities Data Model, 2.4.4

M

materialized views

compressing, 3.5.4

in Oracle Utilities Data Model, 3.5

indexing, 3.5.2

partition change tracking, 3.5.3

partitioning, 3.5.3

refresh options, 3.5.1

metadata management

repository, 6.1.2, 6.2

with Oracle Utilities Data Model, 6.1

metadata repository, 6.1.2

browsing, 6.2

with Oracle Utilities Data Model, 6.2

N

naming conventions

for physical model of Oracle Utilities Data Model, 2.2.2

O

Oracle Utilities Data Model

access layer, 2.1

components of, 1.1.1

customizing, 1.2

customizing physical model, 2.1, 2.2, 2.2.1, 2.2.2, 2.4

dashboards, 5.2

data governance, 1.3.2

dimensional components, 3.4

ETL, 4.1

fit-gap analysis, 1.4

foundation layer, 2.1

implementing, 1.2

intra-ETL, 4.1

metadata management, 6.1

metadata repository, 6.1.2, 6.2

Oracle products used by, 1.1.2

physical layers of, 2.1

pre-implementation tasks, 1.3

querying, 5.3

reporting, 5.1, 5.3

sample reports, 5.2

source-ETL, 4.1, 4.2, 4.2.1.2, 4.2.1.3, 4.2.1.4, 4.2.1.5

staging layer, 2.1

tablespaces, design recommendations, 2.4.1

Oracle Utilities Data Model implementers

prerequisite knowledge for, 1.3.1

Oracle Utilities Data Model warehouse

configuring, A.2

sizing, A.1

P

parallel execution

enabling for a session, 2.4.8.1

enabling for DML operations, 2.4.8.2

in Oracle Utilities Data Model, 2.4.8

partition change tracking, 3.5.3 partition exchange load, 4.2.1.7.5 partitioned indexes in Oracle Utilities Data Model, 2.4.6 partitioning

cubes, 3.4.8

for easier data access, 2.4.7.2

for join performance, 2.4.7.3

for manageability, 2.4.7.1

for source-ETL, 4.2.1.7.5

indexes, 2.4.6

materialized views, 3.5.3

tables, 2.4.7

partitions, changing, 2.4.1 physical model of Oracle Utilities Data Model

characteristics of, 2.1, 2.2, 2.2.1

customizing, 2.2.2

general recommendations for, 2.4

Q

querying Oracle Utilities Data Model, 5.3

R

refreshing

materialized views, 3.5.1

reporting

Oracle Utilities Data Model, 5.1, 5.3

reports

approaches to, 5.1

As Is, 5.6.1

As Was, 5.6.1

troubleshooting performance, 5.5

reports, Oracle Utilities Data Model

creating new, 5.8

S

sample reports

customizing, 5.2

sizing

Oracle Utilities Data Model warehouse, A.1

source-ETL

exception handling, 4.2.1.6

jobs control, 4.2.1.5

loading considerations, 4.2.1.7

Oracle Utilities Data Model, 4.1, 4.2, 4.2.1.2, 4.2.1.3, 4.2.1.4, 4.2.1.5, 4.2.1.7

parallel direct path load, 4.2.1.7.4

partitioning for, 4.2.1.7.5

workflow, 4.2.1.5

staging layer, 2.1

Oracle Utilities Data Model, 2.1

star queries, optimizing, 5.4 subtypes

defining tables for, 2.4.3

physical implementation of, 2.4.3

supertypes

defining tables for, 2.4.3

physical implementation of, 2.4.3

surrogate keys

in Oracle Utilities Data Model, 2.4.4

T

tables

aggregate, 3.3

compressing, 2.4.2, 2.4.2.1, 2.4.2.1.1

derived, 3.2

partitioning, 2.4.7

tablespace in Oracle Utilities Data Model, 2.4.1

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