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ETL Test Automation: Why It Makes Sense for Most Organizations

Extract/Transform/Load (ETL) testing is important in terms of data warehousing and data migration because it validates efficient data loading from a source system to the data warehouse. It offers a consolidated view of the data to the enterprises, thus, allowing them to make better decisions.

Because companies are constantly releasing new applications, too many of them are using ETL test processes manually, which makes them very labor-intensive and error-prone.Also, organizations may be using the wrong tools for managing vital parts of releases for highly visible, usually customer-facing applications. This means risk to confidential data, customer loyalty, and the brand. This makes it important for organizations to consider ETL  test automation.

Reason to Automate ETL Testing

Test automation is important to ensure a high level of data quality. This is especially vital for business intelligence projects. Automating ETL tests enables frequent smoke and regression testing without much user intervention. Aside from helping perform tests, automation can also assist with designing and managing them. Also, automated testing can rapidly run a lot of test case combinations using a few scripts. The automated tests can be used again with minimum efforts for regression testing when newer versions of the source system are available.

How to Automate ETL Tests?

To automate ETL testing, organizations must invest in a dedicated tool for testing their ETLs. Some solutions are available to offer complete and effective data validation and ETL testing without requiring programming skills. If the ETL solution provides does not provide this tool, a more generic testing solution may be available out there. While test automation can be expensive, they can become cost-effective in the longer term, especially when used repeated in regression testing.

Things to Keep in Mind when Automating ETL Tests

Organizations must remember that not all ETL testing is suitable for automation. A great etl testing strategy is to determine the inefficiencies that may be fixed with automated testing. Automation benefits QA teams that spend plenty of time on regression testing.

Moreover, it is imperative to establish a business case for automated testing. In general, automated testing comes at an extra cost over manual testing. Thus, IT should make the case first to convey the value to the business. After assessing the current state and requirements within the IT department, organizations must investigate the tools that fit their testing processes and environments. Their options may include open source, vendor, in-house, or a combination of the tools.

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