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ETL is a deep and complex process. This discussion has only touched the surface. Once you are familiar with the basic concepts presented here, you may be interested in digging deeper. If you are using a packaged ETL solution, you will want to read the documentation to learn the specifics of your product. For further general discussion, consult the following resources: Ralph Kimball et al. break down the ETL process into 34 subsystems, and describe how to plan and execute their development. See s 9 and 10 of the Data Warehouse Lifecycle Toolkit, Second Edition (Wiley, 2008), by Ralph Kimball, Margy Ross, Warren Thornthwaite, Joy Mundy, and Bob Becker. Ralph Kimball and Joe Caserta also provide an expanded book-length treatment of this topic in The Data Warehouse ETL Toolkit: Practical Techniques for Extracting, Cleaning, Conforming, and Delivering Data (Wiley, 2004). This chapter mentioned Kimball and Ross s publishing metaphor for data warehousing. You can find it in 1 of The Data Warehouse Toolkit, Second Edition (Wiley, 2002), by Ralph Kimball and Margy Ross. Integration of aggregate processing into the ETL process is described in s 5 and 6 of Mastering Data Warehouse Aggregates (Wiley, 2006) by Chris Adamson.

It s extremely important to stay current on operating systems. Anyone who has ever heard of a Service Pack or hot fix is aware that operating systems are imperfect and require regular updates. Microsoft and other vendors make updates available in several ways. Let s take a look at how you can find, install and make the best of these updates. To support the users of its products better, Microsoft often releases patches, updates, and free options for its OSs and applications. Clearly, this book would be limited in its usefulness if it assumed that you had already installed all of the latest and greatest add-ons. In the technical chapters of this book, I have assumed that readers are using the following: w Windows NT Server or Workstation with Service Pack 4. For chapters that reference it, Windows NT Option Pack (including IIS 4 and related components). Windows 95/98 with the latest Service Pack/Service Release.

Figure 2-2

As database management became popular during the 1970s and 1980s, a handful of popular data models emerged. Each of these early data models had advantages and disadvantages that played key roles in the development of the relational data model. In many ways the relational data model represented an attempt to streamline and simplify the earlier data models. In order to understand the role and contribution of SQL and the relational model, it is useful to briefly examine some data models that preceded the development of SQL.

In most cases, changes with other operating system versions will be minimal, so it may not be absolutely necessary for you to install all of the upgrades covered in this section.

detail possible. This contrasts with the Inmon approach, where the enterprise data warehouse is designed using the principles of ER modeling. Second, the dimensional data warehouse may be accessed directly by analytic systems. Although it is not required, this is explicitly permitted by the architecture. The concept of a data mart becomes a logical distinction; the data mart is a subject area within the data warehouse. In Figure 2-2, this is represented by the box that highlights a subset of the tables in the dimensional data warehouse. These two key differences are often tempered by accepted variations in the architecture. The construction of a dimensional design from a variety of operational data sources can be challenging, and ETL developers often find it useful to design a multi-step process. Sometimes, a set of tables in third normal form is an intermediate step in this process. Kimball considers this an acceptable feature of a dimensional data warehouse, provided that these staging tables are not accessed directly by any processes other than the ETL process. When such a set of tables is in place, the dimensional data warehouse comes to resemble the Corporate Information Factory more closely. Both contain a normalized repository of data not accessed by applications, and dimensional representations that are accessed by applications. In another accepted variation in the architecture, architects choose to insulate the dimensional data warehouse from direct access by analytic applications. In such cases, new data marts may be constructed by extracting data from the dimensional data warehouse. These data marts may aggregate the dimensional data, or even reorganize it into new dimensional structures. Again, this variation increases the resemblance to the Corporate Information Factory, where data marts are seen as separate entities from the integrated repository of atomic data.

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