![]() This information is then compiled into detailed test cases - step-by-step instructions for running a test.Īnalyze ETL process documentation. A test strategy is a document that lists information about why we’re testing, what methods we’re going to use, what people or tools we will need for it, and of course, how long it will take. ![]() The main idea behind test preparation is analyzing the ETL process logic and transformation rules and then designing a test strategy based on it. This means that ETL testing is mostly done manually, though we will talk about automation tools further in the article. Tons of planning are involved and a tester should have an intimate knowledge of how this particular pipeline is designed and how to write complex test cases for it. Here, you focus on testing the end-to-end process. It’s virtually impossible to take the process apart and do unit testing (checking each piece of code). Testing the ETL process is different from how regular software testing is performed. Now, how do we ensure that data was safely mapped, transformed, and delivered to its destination? system performance bugs when multiple users or high data volumes are not supported, and so on.input/output bugs when invalid values are accepted and valid ones are rejected.nonstandard formats and inconsistent formats between source and target databases.dirty data that doesn’t conform to data mapping rules.invalid values in source databases that result in missing data at a destination.Here are some common errors that can be found during ETL testing: Because in its essence, we’re confirming that the information in your Business Intelligence reports address the exact information pulled from your data sources. The terms data warehouse testing and ETL testing are often used interchangeably and that’s not a huge mistake. Second, sometimes the volumes of this data are huge and the number of their sources can keep growing.Īnd third, the mapping process that connects data fields in sources and destination databases is prone to errors: There are often duplicates and data quality issues.īasically, the testing makes sure that the data is accurate, reliable, and consistent throughout its migration stages and in the data warehouse - along the whole data pipeline. And the data warehouse may be of a different type too. And there’s a big risk that might happen.įirst, it's because data is often collected in myriads of formats from tons of different (heterogeneous) sources. And ETL testing ensures that nothing has been lost or corrupted along the way. What is ETL testing and why do we need it?Īs you probably know, the ETL or Extract, Transform, and Load process supports the movement of data from its source to storage (often a data warehouse) for future use in analyses and reports. If you’re all set, let’s start by understanding the importance of testing your ETL process. Or watch a 14-minute explainer on data engineering: What is Data Engineering: Explaining the Data Pipeline, Data Warehouse, and Data Engineer Role What is ETL Developer: Role Description, Process Breakdown, Responsibilities, and Skills But before you dive in, we recommend reviewing our more beginner-friendly articles on data transformation:Ĭomplete Guide to Business Intelligence and Analytics: Strategy, Steps, Processes, and Tools Here, we introduce you to ETL testing - checking that the data safely traveled from its source to its destination and guaranteeing its high quality before it enters your Business Intelligence reports. How do you trust data with any transformative decisions when there’s a chance that some of it has been lost, or is incomplete, or is simply irrelevant to your business situation? Because as withany system, data processing is subject to mistakes. Which is a good thing! When data is subjective and accurate, it gives us a better understanding than we can fathom with our human brains.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |