Oracle8i Data Warehousing Guide
Release 2 (8.1.6)

Part Number A76994-01





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Data Marts

This chapter contains information useful for building and using data marts, including:

This chapter is not meant to be a substitute for other Oracle or non-Oracle documentation regarding data marts. It is simply an overview. See the Data Mart Suites documentation for further details.

What Is a Data Mart?

A data mart is a simple form of a data warehouse that is focused on a single subject (or functional area), such as Sales or Finance or Marketing. Data marts are often built and controlled by a single department within an organization. Given their single-subject focus, data marts usually draw data from only a few sources. The sources could be internal operational systems, a central data warehouse, or external data.

How Is It Different from a Data Warehouse?

A data warehouse, in contrast, deals with multiple subject areas and is typically implemented and controlled by a central organizational unit such as the Corporate Information Technology (IT) group. Often, it is called a central or enterprise data warehouse. Typically, a data warehouse assembles data from multiple source systems.

Nothing in these basic definitions limits the size of a data mart or the complexity of the decision-support data that it contains. Nevertheless, data marts are typically smaller and less complex than data warehouses; hence, they are typically easier to build and maintain. The following table summarizes the basic differences between a data warehouse and a data mart:

  Data Warehouse  Data Mart 



Line-of-Business (LoB) 



Single Subject 

Data Sources 



Size (typical) 

100 GB-TB+ 

< 100GB 

Implementation Time 

Months to years 


Dependent, Independent, and Hybrid Data Marts

Three basic types of data marts are dependent, independent, and hybrid. The categorization is based primarily on the data source that feeds the data mart. Dependent data marts draw data from a central data warehouse that has already been created. Independent data marts, in contrast, are standalone systems built by drawing data directly from operational or external sources of data or both. Hybrid data marts can draw data from operational systems or data warehouses.

Dependent Data Marts

A dependent data mart allows you to unite your organization's data in one data warehouse. This gives you the usual advantages of centralization. Figure 20-1 illustrates a dependent data mart.

Figure 20-1 Dependent Data Mart

Independent Data Marts

An independent data mart is created without the use of a central data warehouse. This could be desirable for smaller groups within an organization. It is not, however, the focus of this Guide. See the Data Mart Suites documentation for further details regarding this architecture. Figure 20-2 illustrates an independent data mart.

Figure 20-2 Independent Data Marts

Hybrid Data Marts

A hybrid data mart allows you to combine input from sources other than a data warehouse. This could be useful for many situations, especially when you need ad hoc integration, such as after a new group or product is added to the organization. Figure 20-3 illustrates a hybrid data mart.

Figure 20-3 Hybrid Data Mart

Extraction, Transformation, and Transportation

The main difference between independent and dependent data marts is how you populate the data mart; that is, how you get data out of the sources and into the data mart. This step, called the Extraction-Transformation-Transportation (ETT) process, involves moving data from operational systems, filtering it, and loading it into the data mart.

With dependent data marts, this process is somewhat simplified because formatted and summarized (clean) data has already been loaded into the central data warehouse. The ETT process for dependent data marts is mostly a process of identifying the right subset of data relevant to the chosen data mart subject and moving a copy of it, perhaps in a summarized form.

With independent data marts, however, you must deal with all aspects of the ETT process, much as you do with a central data warehouse. The number of sources are likely to be fewer and the amount of data associated with the data mart is less than the warehouse, given your focus on a single subject.

The motivations behind the creation of these two types of data marts are also typically different. Dependent data marts are usually built to achieve improved performance and availability, better control, and lower telecommunication costs resulting from local access of data relevant to a specific department. The creation of independent data marts is often driven by the need to have a solution within a shorter time.

Hybrid data marts simply combine the issues of independent and independent data marts.

See Chapter 10, "ETT Overview", for further details regarding the ETT process.

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