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Configuring and managing hard disk storage resources can be quite a challenge, because business needs increase very rapidly. The new Disk Management administrative interface enables you to easily view physical and logical configurations on both local and remote servers. This tool can be accessed through the Computer Management tool. Figure 3-22 shows an example of the information you can see. Using this interface, you can initialize, format, and reconfigure storage resources. To make some of the more complex tasks much more intuitive, a wizard can be used for operations such as creating partitions. One of the challenges in working with software disk configurations is that many changes required a reboot of the server. Although this is acceptable in smaller environments, mission-critical servers cannot afford the amount of downtime. To address this issue, hard disks in Windows 2000 can be configured as dynamic disks. Dynamic disks cannot contain partitions or logical drives and are not accessible from earlier OSs (such as DOS, Windows 95/98, or NT 4).

Like the Corporate Information Factory, this architecture begins by assuming a separation of the operational and analytic systems As before, operational systems appear on the far left of the diagram Again, these may incorporate data stores that are relational and nonrelational, and are likely to be numerous Moving to the right, an ETL process consolidates information from the various operational systems, integrates it, and loads it into a single repository If that sounds familiar, it should The Corporate Information Factory has an analogous process The dimensional data warehouse in the center of Figure 2-2 is the end result of the ETL process It is an integrated repository for atomic data Again, that should sound familiar The same definition was given for Inmon s enterprise data warehouse It contains a single view of business activities, as drawn from throughout the enterprise.

Work on the official SQL standard began in 1982, when ANSI charged its X3H2 committee with defining a standard relational database language. At first the committee debated the merits of various proposed database languages. However, as IBM's commitment to SQL increased and SQL emerged as a de facto standard in the market, the committee selected SQL as their relational database language and turned their attention to standardizing it. The resulting ANSI standard for SQL is largely based on DB2 SQL, although it contains some major differences from DB2. After several revisions, the standard was officially adopted as ANSI standard X3.135 in 1986, and as an ISO standard in 1987. The ANSI/ISO standard has since been adopted as a Federal Information Processing Standard (FIPS) by the U.S. government. This standard, slightly revised and expanded in 1989, is usually called the "SQL-89" or "SQL1" standard.

Figure 3-22.

It stores that information in a highly granular, or atomic, format The dimensional data warehouse differs from the enterprise data warehouse in two important ways First, it is designed according to the principles of dimensional modeling It consists of a series of star schemas or cubes, which capture information at the lowest level of.

3:

The fact table load process takes transactions from the source system, calculates facts, identifies the appropriate foreign keys, and inserts records into the star. The most complex part of this process involves identification of the appropriate foreign key values. Source systems supply only a natural key, but the fact table must use foreign keys to refer to the dimensions. For each fact in a source system, the natural keys that identify dimensional context must be replaced by surrogate keys that identify rows in dimension tables. Because type 2 changes are possible, there may be multiple rows in the dimension table for a particular value. Figuring out which surrogate key to use is an important task and may be assisted by some schema design tweaks.

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Data Protection in Windows NT/2000

Much of the difficult work of loading a star schema is done in the dimension loads, where slow change processing is performed. Relative to the dimension load, the fact table load may seem simple. The fact table load is governed by the following requirements: Extract data from the source systems Compute the facts Aggregate facts to match the grain of the fact table Obtain surrogate keys for each of the dimensions Load fact table records into the warehouse A process flow supporting these requirements is depicted in Figure 17-4. Once again, this is a logical breakdown, meant to guide the discussion of the load process. The specific tasks and techniques used will vary based on source and target systems and design, the capabilities of the ETL tool being used, and developer preferences.