Only a vendor could love the advice: "No one tool will do the job, you need to buy both." Though unfortunate from an end-user cost-control point of view, there are specific instances where that is the case for extract, transform and load (ETL) and enterprise application integration (EAI) technologies. Having said that, it behooves prospective buyers to know the difference because in many instances only one class of technology is needed.

Let's get our bearings. The distinctions between transaction-centric and query-centric systems and between operational and decision support systems remain fundamental. If a firm's requirements lie more in the area of synchronizing the transactions hitting an application server with related operational systems, then it is likely an EAI infrastructure will take priority. A data integration platform is not a substitute for an application server or its dynamic functionality. The latter is required to handle any heavy transactional workload, just as a transaction processing monitor was required prior to the explosion of the Internet. The ETL functionality is not directly customer – or business-to-business (B2B) – order facing; rather, it is positioned in one of the layers, such as the data warehouse itself, between the front and back office. Tools such as DataStage XE (Ascential), DecisionStream (Cognos), ETI- Extract, PowerCenter (Informatica) and Sagent ship with dozens, even hundreds, of predefined functions to drag and drop data element mappings. This activity precisely determines the runtime environment of the transformation server or, in the case of code-generating tools (such as ETI-Extract, Oracle Warehouse Builder, SAS Warehouse Administrator or DataStage XE/390), actually generates code (and environmental scripts including JCL) in C, COBOL or ABAP. The data integration features of ETL are such that their design workstations avoid the hand coding to an application programming interface (API) of many of the EAI products. The ETL approach provides audit trails, impact analysis at the meta data level and avoids the need for point-to-point adapters unless dealing with enterprise resource planning or customer relationship management operational silos.

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