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SQL is the “New” Industry Standard Language for ETL
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Information access challenges are not easy to address. While the volumes of data continue to grow in organizations, the flexibility to access that data has not kept pace. This paper discusses managing your Information Assets.
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2004 is set to be the year when a major shift will occur in the ETL market. Last year saw major RDBMS vendors gain momentum with new and low-cost versions of their ETL software that generate SQL scripts. Yet, major market analysts consider ETL tools from RDBMS vendors to be a viable solution only for smaller projects and in situations where the source and target RDBMS are homogeneous.
Understandably the largest players in the ETL market have based their product strategy and positioning on the claim that SQL is not properly suited to perform ETL tasks. They have pushed proprietary languages and expensive software as the only solution for successful ETL projects.
In reality, custom code is still involved in nearly two thirds of all ETL projects because IT teams actually use the enhanced capabilities of SQL in the latest version of their RDBMS to extract data from databases, then transform and load it into a data warehouse.
What is the right approach? Will SQL become the industry standard language for ETL in 2004 and push proprietary languages into obsolescence?
The traditional engine-based ETL approach
For at least 10 years, most ETL software tools were based on proprietary engines, and traditional ETL vendors brought a strong value proposition to the market. In the 90s, the most widely used versions of the major RDBMSs, such as Oracle, Ingres, Informix, and SQL Server from Sybase and Microsoft, provided a reliable means for storing and accessing large volumes of data without having to define the physical path that enabled those actions. Nevertheless, the SQL languages supported by the RDBMS releases of the 90s were not rich enough to handle the complex transformations required by customers who needed to build a data warehouse. Hence, during that decade, ETL software vendors were not able to utilize SQL and RDBMS as engines to perform transformations. The only viable technical solution was to build costly proprietary-engine-based software with complex proprietary languages to perform the ETL work.
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Oracle #1 in Business Analytics According to IDC Research
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