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SQL-based relational databases were a clear choice for the warehouse data store because of their flexible query processing. A series of new companies was formed to build the data extraction, transformation, and database query tools needed by the data warehouse model. In addition, DBMS vendors started to focus on the kinds of database queries that customers tended to run in the data warehouse. These queries tended to be large and complex such as analyzing tens or hundreds of millions of individual cashregister receipts to look for product purchase patterns. They often involved time-series data for example, analyzing product sales or market share data over time. They also tended to involve statistical summaries of data total sales, average order volume, percent growth, and so on rather than the individual data items themselves.

Figure 17-11

With Network Monitor, you can also save on your hard drive the information that you captured in a file. This information can then be recalled later for analysis, if required.

The solution is to add a housekeeping dimension table that can keep track of updates to the fact table. This table maintains some of the same information that can be stored directly in dimension tables: the date the record was created or updated, and the name of the process or user who did so. This same table is a natural place to maintain other statistics about the ETL process execution itself. Figure 17-11 illustrates how this technique is used to add housekeeping data to a fact table. The load_info dimension contains the housekeeping data; each fact table row contains a load_key value that refers to the appropriate row. If a fact table row is altered, this key value can be replaced. Developers are cautioned not to get carried away with housekeeping data. While it can be useful, it is easy to extend the solution unnecessarily. One might be tempted to generalize this solution, for example, to log changes to any table fact or dimension. This may seem elegant, but it introduces undue complexity to a solution that is intended primarily for the support of ETL processing, quality assurance, or data forensics. Similarly, the addition of metrics to these tables likely duplicates information that is already present in log files maintained by the ETL tools. Too much time spent developing stars that describe the load processes may result in your being admonished for neglecting the load process itself!

- 37 -

Summary

You don t have to be running Windows NT to do performance monitoring. Windows 95 and Windows 98 were designed to be consumer/end-user operating systems, but they can still provide valuable performance information. Although the tools are somewhat different (and much more limited), Windows 95/98 System Monitor can be used to find basic information on the status of your system. To run System Monitor, select Start | Programs | Accessories | System Tools | System Monitor. If the program is not installed, you can add it by using the Add/Remove Control Panel item. The interface for these tools is as user-friendly as their Windows NT counterparts, and a Help file is available to assist you in determining the usefulness of information. Table 3-6 lists some useful items to monitor. Figure 3-33 shows Windows 98 System Monitor displaying basic statistics. This information can be useful to determine, for example, whether your system is overloaded or whether a dial-up adapter is performing poorly due to line noise or a low connection speed.

To address the specialized needs of data warehousing applications (often called "Online Analytical Processing" or OLAP), specialized databases began to appear. These databases were optimized for OLAP workloads in several different ways. Their performance was tuned for complex, read-only query access. They supported advanced statistical and other data functions, such as built-in time-series processing. They supported precalculation of database statistical data, so that retrieving averages and totals could be dramatically faster. Some of these specialized databases did not use SQL, but many did (leading to the companion term "ROLAP," for Relational Online Analytic Processing). As with so many segments of the database market, SQL's advantages as a standard proved to be a powerful force. Data warehousing has become a one-billion-dollar plus segment of the database market, and SQL-based databases are firmly entrenched as the mainstream technology for building data warehouses.

It is useful for everyone on the data warehouse team to have a basic understanding of the ETL process. It illustrates the influence of the schema design on processing complexity. Conversely, it also illustrates the potential influence that the ETL process may have on the design, suggesting potential adjustments that make things easier to process. A high-level understanding of the load process also affords ETL developers an opportunity to review key concepts and avoid common mistakes.