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Repeatable Process The hybrid response is repeatable. Each time a company changes, the same two-step process is followed. First, all existing rows with the same natural key have their _current value updated. Second, a new row is added, with _current and _historic initialized to the new value. Suppose that Apple decides to change its name again. The iPod and iPhone have been so successful they might rename the company iApple Inc. Figure 8-9 shows how this change would be processed. First, the existing records in the dimension table for Apple, which is company BB770, are updated with the new company_name_current. This time around, there are two records to be updated: those identified by surrogate keys 1011 and 2822. That takes care of the type 1 processing. Second, a new row is added to the table for iApple. This row records the new name, iApple Inc., in both the company_name_current and company_name_historic columns.

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.

15 1

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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.

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.

However, under APB 25, if either the exercise price or the number of shares is unknown or variable, then the measurement date is postponed until both these facts are known For example, if the.

$140.00 Dow Industrial Average $120.00 10,000 Losing Divergence 8,000 $80.00 AT&T Corp. NYSE: T $60.00 4,000 $40.00 Short the Turn $20.00 2,000 6,000 12,000

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.

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

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