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PART VI
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Last, this chapter will recap the various forms of housekeeping columns that have been encountered throughout the book. These special columns streamline the reporting process, ETL development, or the quality assurance process. Some additional columns can be added to assist in the development of derived and aggregate tables, and to provide a record of the activities that last touched each row.
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This book uses the term ETL to describe any process that moves data from one system or data structure to another. A variety of other terms are used to describe this activity, including data movement, data integration, and data presentation. The term ETL is used for convenience; it is not meant to imply a particular data warehouse architecture or a particular kind of software product. The ETL process is complex, and its development is a significant challenge. The way this process is organized varies tremendously. It is influenced both by the specific tools used to develop it, as well as the data warehouse architecture in which it functions. Though the details differ widely, every solution that populates a star schema with information must address some fundamental requirements.
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Development of an ETL process is one of the most challenging tasks in data warehousing. It is hard to overstate its complexity. It often consumes the lion s share of project resources and is a primary source of project risk. The magnitude of the ETL task accounts for its complexity. The ETL process must work with a variety of different source systems, based on heterogeneous technologies, to create a single integrated view of information. It must do this with data that is often of dubious quality. It must function within tightly prescribed processing windows and have minimal impact on operational systems. It must be fully automated, able to process exceptions, and able to recover from errors. A heavy emphasis on the load process is consistent with basic principles of data warehousing in general, and star schema design in particular. By doing the hard work of restructuring operational data in advance, it is easier to analyze it at query time.
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The ETL process is a coordinated effort between numerous tools and utilities that are configured and assembled to load the star schema as required by its design documentation. Packaged software products known as ETL tools are often leveraged to accomplish some or all of this process. When used, these tools are likely to be supplemented by other utilities as well. Some organizations opt not to leverage these specialized tools, instead incorporating hand-coded programs written using general-purpose tools. Regardless of the underlying technologies, the entire process is referred to here as ETL.
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Different data warehouse architectures organize the task of data movement in different ways. As you learned in 2, Ralph Kimball s dimensional data warehouse calls for a
17 Design and ETL 405
logical repository of atomic data, drawn and integrated from operational data sources. This information is dimensional in structure and may be queried directly. Optional second-line data marts, composed of derived and aggregate tables, may supplement this information. Both are populated by ETL processes. W.H. Inmon s Corporate Information Factory architecture also calls for the establishment of an integrated repository of atomic data. This repository is called the enterprise data warehouse. Unlike Kimball s repository, it is not dimensional, nor is it queried directly. Instead, this repository serves as the data source for star schema data marts. All layers are populated by ETL processes. (Note that Inmon reserves this term for the first stage only; the second is referred to as data presentation.) Stand-alone data marts do not involve an integrated repository of enterprise data. They are developed without an enterprise context, focused instead on a single subject area. An ETL process draws data directly from operational sources to populate the data mart. Although these three architectures are organized differently, all require that information from source systems eventually make its way into a star schema. A developer charged with populating a star schema in a Corporate Information Factory may have an easier job: there is a single source of data, which has already been integrated and standardized. In all cases, however, the same fundamental processing requirements must be met.
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