All testers must be provided access to complete data, obfuscated where necessary.Senior management must create regulations that prevent any ETL process from deployment in production must be accompanied by a test and a passing result.We will divide the framework into two primary components process and tools. With iceDQ software, we recommend a data-driven + rules-based testing framework. While ETL test Automation is important you will need some kind of framework consisting of both practices and ETL testing tools to improve QA professionals’ productivity and effectiveness. The data process and orchestration issues can only be found during testing. Reduction of data issues in production: Catching data issues early before they go into production is essential. As companies are moving towards fully automated deployment so is the need for ETL testing tools that can integrate with the automated releases. DevOps Integration: Creating CICD pipelines need automated ETL testing tools.Even though test automation software they were never designed to test ETL process. On one side you have mobile and web applications on the other side you have data integration or ETL processes. UI-based application testing software cannot test ETL process: There are many types of software.Digitization and Automation are the only way forward. Digitization and Automation requirements: While there is a growth in the data requirements, there is also a lowering of budget and project timelines.You need to recall the old tests combine them with the new ones and certify the ETL tool. Hence, regression ETL testing becomes extremely complex and costly in the absence of automation testing. Iterative change testing: ETL transformations keep changing based on new business requirements.Hence, the ETL test automation platform must have the ability to connect both the data source and destination database or files to compare and validate the data. The data generated by the ETL is dependent on the input data and the transformations. Instead, ETL testing tools validate the data generated by the ETL, and if the data is valid, only the ETL process is certified. Complexity of the ETL process: ETL testing tools cannot directly test the ETL processes.It is very difficult to test, track and report the validation of the ETL. Increase in the number of ETL processes: There are thousands of ETL processes that are under development, deployed, or enhancement.This can result in missing critical and essential ETL testing scenarios. A manual inspection process also forces the user to test a smaller sample for ETL Testing. Comparing large volumes of data by simply eyeballing is highly prone to error. Exponential increase in data volume: The ETL testing software is also unique as it needs to be scalable to test billions of records. The ETL Testing software must be able to support the testing of changes by supplying some old and new rules to certify the changes. At this point, the operations teams can use the knowledge gained by the automation team and proactively borrow and embed the test to monitor known data traps.Īt some point, new data is discovered or even the business changes might require redevelopment and regression tests. On the right side of the deployment, you are in production. On the left side of the testing involves complete automation and capture of test cases, tests, and integration with DevOps so that the manager gets a complete auditable test result in their hand. This way both left-shift and right-shift approaches are covered. That might be true for the software life cycle but is absolutely not true for data.Įnd-to-end testing spans the development and embedding of some of the test automation in production, also called Whitebox testing. Many believe that once the testing is done and the code is deployed in production that is the end of the testing. An End-to-End ETL test automation tool needs to automate plus support the complete data development lifecycle.
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