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Figure 3-9 Documenting the slow change rules for a dimension table
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should respond. For example, a change to a customer s marital status may be treated as a type 1 change if the operational system records it as error correction or a type 2 change if the change is logged as a result of an actual change in marital status. TIP For each dimension attribute, choose and document the appropriate slow change response. If you are uncertain, the type 2 response is safest. When a source system captures the reason for a change, a single attribute may drive either type of response. In addition to the type 1 and type 2 techniques introduced in this chapter, additional responses to source data changes are possible. Options include the type 3 response, hybrid responses, and time-stamped variations. Though less common, these techniques meet additional analytic challenges that will be discussed in 8.
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Action Type 1 Update Dimension Effect on Facts Restates History
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Figure 3-10 Summary of slowly changing dimension techniques
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In addition to changing the history of facts, type 1 changes introduce other complications. If a dimension attribute is designated as type 1, and it is not fully dependent on the table s natural key, the update response must be carefully evaluated. For example, a product table may include a brand code (type 2) and brand name (type 1). A change to a product s brand name may result in an update, if the name associated with the brand code was changed, or may result in a new row, if the product is to be associated with a different brand altogether. This situation will require documentation that is more detailed than what is shown in Figure 3-9. Additionally, any type 1 attribute may introduce problems for the maintenance of aggregate tables or cubes that draw data from the star. This phenomenon will be explored in 15.
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Implementation of Slow Change Processing
When a dimension table exhibits multiple response types, as in Figure 3-9, ETL developers must factor in a variety of possible situations. For example, a type 1 change may require updating multiple rows in the dimension table. If Sue Johnson s date of birth had been corrected after she had moved, for example, the type 1 change to her birth date would apply to multiple rows. Otherwise, some versions of Sue would indicate one date of birth, while others indicate another. The ETL developer must also consider the possibility that type 1 and type 2 changes occur at the same time. For example, it may be that Sue moves (type 2) and has her date of birth corrected (type 1) on the same day. A single source record for Sue will contain the seeds for both type 1 and type 2 responses. It is important to acknowledge the fact that slow change processing makes the lives of ETL developers very difficult. Slow change requirements impact every part of the loading process, both in terms of complexity and in terms of processing time. ETL developers may face the additional challenge of determining whether changes have taken place at all. These activities are discussed in 17, along with some common design tweaks that can help streamline the process.
Cubes
Dimensional models are not always implemented in relational databases. A multidimensional database, or MDB, stores dimensional information in a format called a cube. The basic concept behind a cube is to precompute the various combinations of dimension values and fact values so they can be studied interactively.
Multidimensional Storage vs. Relational Storage
The primary advantage of the multidimensional database is speed. A cube allows users to change their perspective on the data interactively, adding or removing attributes to or from their view and receiving instantaneous feedback. This process is often referred to as Online
Part I
Type 1 Complications
PART I Fundamentals
Analytical Processing, or OLAP. OLAP interaction with a cube is highly responsive; there is instantaneous feedback as you slice and dice, drill up and drill down. In contrast, a star schema is interacted with through a query-and-response paradigm. Each change in the information detail on display requires the issuance of a new query. Another advantage of the multidimensional database is that it is not hampered by the limitations of SQL. Because it specializes in the storage of facts and dimensions, it can offer interfaces to ask for information that SQL does not traditionally handle well. MDBs were providing running totals, rankings and other statistical operations long before these capabilities were added to SQL. Multidimensional databases may also offer specialized support for recursive hierarchies, which may be ragged, something that requires a bridge table in the star schema world. (More on this in 10, Recursive Hierarchies and Bridges. ) Of course, all this capability comes with a cost. As the number of dimensions and their values increase, the number of possible combinations that must be precomputed explodes. This limits the ability of the cube to scale with large volumes of data. Typical measures to stem this limitation invariably reduce some of the benefits offered by the cube. Data in an MDB is accessed through an interface, which is often proprietary, although MDX has gained wide acceptance as a standard. Still, the ability to write queries in this environment is a skill that is not as widely available. In contrast, there is a large pool of information technology professionals who understand SQL, and a wider variety of reporting tools that support it. To some, this is another disadvantage of the MDB. Figure 3-11 summarizes the differences in these technologies.
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