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If you re managing more than a few servers in your environment, you ll find it difficult to implement a new data protection plan for all of them. When dealing with a large task, it s often a good idea to break it into more manageable pieces. A series of smaller tasks has several benefits. First, small successes are psychologically encouraging. If you re not trying to fix all of your problems at once, you can focus on just a few tasks and devote your full attention to them. Second, in prioritizing the milestones, be sure to get management input not only from the IT department, but also from the affected department heads. In developing a data protection plan, the iterative approach involves establishing a baseline plan and then refining it over time. Table 2-3 shows an example of how a complex task protecting data throughout a large organization can be made more manageable. Notice that we start with a baseline (a minimal level of data protection), and then refine the process to make sure that systems are protected appropriately. Then, we focus on improving performance in areas where this can improve the users experience. Finally, we choose to improve upon the data protection plan itself by measuring the effects of changes and feeding them back into the overall strategy. If your environment has any level of data protection, you have probably already met some of the requirements. If that s the case, be sure to document which areas are already

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This is enough additional work that many ETL developers opt for a simpler brute-force approach: after updating the base schema, drop and re-create the aggregate Often, this proves to be less processing-intensive than the alternative Some tools that automate the creation of aggregate tables or cubes operate in this manner; if the aggregate contains a type 1 attribute, the cube is destroyed and rebuilt each time a change occurs TiP Type 1 changes can significantly complicate aggregate processing If an aggregate star includes a type 1 attribute, it may be best to drop and rebuild the aggregate fact table Another way to avoid this situation is to exclude type 1 attributes from aggregate designs Type 2 changes don t pose the same problem, since they don t restate the context of previously recorded facts..

Task Analyze departmental requirements and form a data protection plan. Verify that all business systems are receiving at least a minimal level of data protection. Fully protect all Marketing servers. Fully protect all Engineering servers. Optimize OLTP backup performance for point-of-sale applications. Evaluate effects of changes and refine the data protection plan based on user feedback. Data Protection Plan Implementation Schedule

Developers can sometimes minimize the impact of a type 1 change if the rollup dimension also includes a type 2 attribute that the type 1 attribute describes. In this case, the type 1 change may be applied only to the appropriate row, and aggregate facts will not require an update. Designers sometimes borrow language from entity-relationship modelers to describe this case: the type 1 attribute in the aggregate dimension is fully dependent on the type 2 attribute. This approach, however, provides new challenges for the ETL process. See Type 1 Complications in 3 for more details.

The SET DEFAULT delete rule tells the DBMS that when a parent row is deleted, the foreign key values in all of its child rows should automatically be set to the default value for that particular column Deletions from the parent table thus cause a "set to DEFAULT" update on selected columns of the child table For the tables in Figure 11-1, this rule can be summarized as "If an office is deleted, indicate that the current office assignment of its salespeople is the default office specified in the definition of the SALESREPS table" There are some slight differences between the SQL2 and DB2 implementations of the delete rules The current DB2 implementation does not support the SET DEFAULT rule; it is only specified by the SQL2 standard The SQL2 standard actually calls the previously described RESTRICT rule, NO ACTION The SQL2 naming is somewhat confusing.

Target Date 03/01/2000 03/17/2000

While this book uses relational designs to illustrate most concepts, 3 pointed out that this is simply a convenience. Most of the principles of dimensional modeling apply equally well, whether implemented in a star schema or a cube. The benefit of summarization, however, differs when it comes to cubes.

It means "if you try to delete a parent row that still has children, the DBMS will take no action on the row" The DBMS will, however, generate an error code Intuitively, the DB2 name for the rule, "restrict," seems a better description of the situation the DBMS will restrict the DELETE operation from taking place and generate an error code The latest release of DB2 supports both a RESTRICT and a NO ACTION delete rule The difference between them is the timing of the enforcement of the rule The RESTRICT rule is enforced before any other constraints; the NO ACTION rule is enforced after other referential constraints Under almost all circumstances, the two rules operate identically.

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