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November 11, 2009

The General View on ETL

Filed under: Data Integration, Data Quality, ETL — Olga Belokurskaya @ 10:13 am

The popularity of ETL (extract, transform, and load) is not so hard to explain. Gathering the data from various sources in one destination, thus providing a unified view on, for example, customer data, or other important data that travels from detached departments, ETL, actually, rescues a company from data chaos. So data stops being a bunch of some contradictory records, and turns into a powerful tool able to improve company’s competitiveness.

Though it sounds so great, choosing and implementing ETL may be quite complex and tiring. Moreover, there should be the right methodology selected, as well as the right data integration model, etc. And this is more than true when ETL and data integration initiatives are started from scratch. Why?

Imagine a huge, ok, not huge, just a big organization.
First, there are lots of departments there.
Second, each department utilizes different applications.
Third, each department has its own rules on how data should be viewed and treated. This point is, actually, crucial one. For the most popular thing companies face starting data integration initiative is the data (sometimes the same data) entered and stored in different formats by different departments. So before integrating the data into one target, the data should be formalized somehow.

About several years ago this was impossible without amounts of hand-coding. Today, as the market for ETL solutions expands, vendors try to implement as much functionality to their tolls as users require. So modern ETL tools vary from simple exclusively extract-transform-load providing tools to multi-functional data integration platforms that allow performing various operations. The choice depends on what companies business needs are and whether this or that tool may provide the data quality just enough to fit those needs.

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