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ETL, ELT, ETLT...How to devise the best test strategy for your Data warehouse applications?

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The success of a data warehouse system doesn't merely depend on how intelligently it can mine data but more often than not it is also based on how quickly it can serve the user's needs. Traditional data warehouse systems or ETL (Extract, Transform, Load) were designed with user output in the mind and the input data had to be processed every time there was a need for a user report to be generated. However, modern methods are more radical. They focus on creating a cleaner input data so that reports can be created 'on-the-go' that fit the purpose rather than a one-fits-all approach. ELT (Extraction, Load, Transformation) is the modern method of designing data warehouse systems.

In the current scenario, more and more organizations are redesigning their data warehouse platforms to ensure correctness, completeness and appropriateness of the data they provide to their external and internal stakeholders. Testing a data warehouse system that is being redesigned is a challenge in itself, especially if it involves change in the process such as ETL to ELT or ETL to ETLT (Extract, Transform, Load, Transform). There is a need to devise a test strategy for testing the existing data, table structures, relationship between data models, integrity of backup databases and the underlying processes. At the same time, it is very important not to ignore the bigger picture ie., ensuring the correct data 'output' to the stakeholders. I have implemented this strategy in some of the complex data warehouse programs and realized that an effective and binding test strategy is extremely critical for the success of those systems. If I have got you interested in the topic, you can read my latest POV at http://www.infosys.com/IT-services/independent-validation-testing-services/features-opinions/Pages/data-warehouse-testing.aspx.



The above information is very useful for all.
thanks for providind an information.

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