The Extract-Transform-Load (ETL) system is the foundation of the data warehouse. A properly designed ETL system extracts data from the source systems, enforces data quality and consistency standards, conforms data so that separate sources can be used together, and finally delivers data in a presentation-ready format so that application developers can build applications and end users can make decisions. This book is organized around these four steps.
The ETL system makes or breaks the data warehouse. Although building the ETL system is a back room activity that is not very visible to end users, it easily consumes 70 percent of the resources needed for implementation and maintenance of a typical data warehouse.
The ETL system adds significant value to data. It is far more than plumbing for getting data out of source systems and into the data warehouse. Specifically, the ETL system:
Removes mistakes and corrects missing data
Provides documented measures of confidence in data
Captures the flow of transactional data for safekeeping
Adjusts data from multiple sources to be used together
Structures data to be usable by end-user tools
ETL is both a simple and a complicated subject. Almost everyone understands the basic mission of the ETL system: to get data out of the source and load it into the data warehouse. And most observers are increasingly appreciating the need to clean and transform data along the way. So much for the simple view. It is a fact of life that the next step in ...