The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling, 3rd Edition

Chapter 19

ETL Subsystems and Techniques

The extract, transformation, and load (ETL) system consumes a disproportionate share of the time and effort required to build a DW/BI environment. Developing the ETL system is challenging because so many outside constraints put pressure on its design: the business requirements, source data realities, budget, processing windows, and skill sets of the available staff. Yet it can be hard to appreciate just why the ETL system is so complex and resource-intensive. Everyone understands the three letters: You get the data out of its original source location (E), you do something to it (T), and then you load it (L) into a final set of tables for the business users to query.

When asked about the best way to design and build the ETL system, many designers say, “Well, that depends.” It depends on the source; it depends on limitations of the data; it depends on the scripting languages and ETL tools available; it depends on the staff's skills; and it depends on the BI tools. But the “it depends” response is dangerous because it becomes an excuse to take an unstructured approach to developing an ETL system, which in the worse-case scenario results in an undifferentiated spaghetti-mess of tables, modules, processes, scripts, triggers, alerts, and job schedules. This “creative” design approach should not be tolerated. With the wisdom of hindsight from thousands of successful data warehouses, a set of ETL best practices have emerged. There is no reason to ...