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16 Business Intelligence → This chapter describes some of the basic ideas in business intelligence. This chapter contains the … following topics: Introduction to Data Warehousing and Business Intelligence Overview of Extraction
Overview of Business Intelligence Features → This section describes the following business intelligence features: Data Warehousing Materialized … entire spectrum of business intelligence and advanced analytical applications. Oracle OLAP is fully … Also: Chapter 16, \"Business Intelligence\" for more information about Oracle Data
Data Warehouse Architecture → systems designed for a particular line of business. Figure 16-3 illustrates an example where
Overview of Bitmap Indexes in Data Warehousing → Bitmap indexes are widely used in data warehousing environments. The environments typically have large amounts of data and ad hoc queries, but a low level of concurrent DML transactions. For such applications, bitmap indexing provides: Reduced response time for large classes of ad hoc queries Reduced storage requirements compared to other indexing techniques Dramatic performance gains even on hardware
SQL for Analysis → Oracle has advanced SQL analytical processing capabilities using a family of analytic SQL functions. These analytic functions enable you to calculate: Rankings and percentiles Moving window calculations Lag/lead analysis First/last analysis Linear regression statistics Ranking functions include cumulative distributions, percent rank, and N-tiles. Moving window calculations allow you to find moving
Overview of Data Mining → , and business planning. As of Oracle Database 11 g, Oracle Data Mining models are implemented as data
Ease of Application Development → By integrating multidimensional objects and analytics into the database, Oracle provides the best of both worlds: the power of multidimensional analysis along with the reliability, availability, security, and scalability of 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
Differences Between Data Warehouse and OLTP Systems → current state of each business transaction. Schema Design Data warehouses often use denormalized or
SQL for Aggregation → Aggregation is a fundamental part of data warehousing. To improve aggregation performance in your warehouse, Oracle Database provides extensions to the GROUP BY clause to make querying and reporting easier and faster. Some of these extensions enable you to: Aggregate at increasing levels of aggregation, from the most detailed up to a grand total Calculate all possible combinations of aggregations
Overview of OLAP Capabilities → Oracle online analytical processing (OLAP) adds power to your SQL applications by providing extensive analytic content and fast query response times. A SQL query interface enables any application to query cubes and dimensions without any knowledge of OLAP.
Security → Because Oracle OLAP is completely embedded in Oracle Database, there is no administration learning curve as is typically associated with standalone OLAP servers. You can leverage your existing DBA staff, rather than invest in specialized administration skills. One major administrative advantage of Oracle's embedded OLAP technology is automated cube maintenance. With standalone OLAP servers, the burden
Reduced Costs → Business intelligence and analytical applications are dominated by actions such as drilling up and … delivers unmatched performance for typical business intelligence applications. Oracle OLAP queries
Characteristics of Data Warehousing → analyze what has occurred. Time Variant In order to discover trends in business, analysts need large
Overview of Extraction, Transformation, and Loading (ETL) → unified information base for business intelligence. Additionally, the data volume in data warehouse … business analysis. To perform this operation, data from one or more operational systems must be extracted … business situation. The same is true for the time delta between two (logically) identical extractions
Change Data Capture → efficiently identifies and captures data that has been added to, updated, or removed from Oracle Database relational tables, and makes the change data available for use by applications. Oftentimes, data warehousing involves the extraction and transportation of relational data from one or more source databases into the data warehouse for analysis. Change Data Ca pture quickly identifies and processes
Overview of Analytic SQL → Oracle has introduced many SQL operations for performing analytic operations in the database. These operations include ranking, moving averages, cumulative sums, ratio-to-reports, and period-over-period comparisons. Although some of these calculations were previously possible using SQL, this syntax offers much better performance. This section discusses: SQL for Aggregation SQL for Analysis SQL for
Unmatched Performance and Scalability → With Oracle OLAP, standard Oracle Database security features are used to secure your multidimensional data. In contrast, with a standalone OLAP server, administrators must manage security twice: once on the relational source system and again on the OLAP server system. Additionally, they must manage the security of data in transit from the relational system to the standalone OLAP system. Unmatched
Table Compression → You can save disk space by compressing heap-organized tables. A typical type of heap-organized table you should consider for table compression is partitioned tables. To reduce disk use and memory use (specifically, the buffer cache), you can store tables and partitioned tables in a compressed format inside the database. This often leads to a better scaleup for read-only operations. Table compression
Full Integration of Multidimensional Technology → Application Express and Business Intelligence Publisher. See Also: Oracle OLAP User's Guide This section
Introduction to Data Warehousing and Business Intelligence → business users. This section includes the following topics: Characteristics of Data Warehousing