It assumes that the data warehouse team has already identified the data that will be extracted, and discusses common techniques used for extracting data from source databases.ĭesigning this process means making decisions about the following two main aspects: This chapter, however, focuses on the technical considerations of having different kinds of sources and extraction methods.

These are important considerations for extraction and ETL in general. Moreover, the source system typically cannot be modified, nor can its performance or availability be adjusted, to accommodate the needs of the data warehouse extraction process. The data has to be extracted normally not only once, but several times in a periodic manner to supply all changed data to the data warehouse and keep it up-to-date.

The source systems might be very complex and poorly documented, and thus determining which data needs to be extracted can be difficult. For example, one of the source systems for a sales analysis data warehouse might be an order entry system that records all of the current order activities.ĭesigning and creating the extraction process is often one of the most time-consuming tasks in the ETL process and, indeed, in the entire data warehousing process. The source systems for a data warehouse are typically transaction processing applications. After the extraction, this data can be transformed and loaded into the data warehouse. This is the first step of the ETL process. Extraction is the operation of extracting data from a source system for further use in a data warehouse environment.

How to extract software tracking