![]() With traditional ETL test planning, there are six phases: They must also test each point between extract and load, including data load from the source extract to staging, staging to transformation and once the data reaches the data warehouse, test data extraction for display and reporting. The QA team must test initial and incremental loads for the entire ETL process beginning with identifying source data to report and portal functions. Preparing for ETL TestingĪ data warehouse implementation must include end-to-end testing. During the analysis phase, the testing team must learn and understand the different stages of the data warehouse implementation including but not limited to:ĮTL testing includes multiple phases, and testing should be executed throughout the lifecycle of the data warehouse implementation, not just at the end. Understanding the ETL Testing ProcessĪ solid understanding of data modeling provides testing teams with information to develop the right testing strategy. Most importantly, the data warehouse is a strategic enterprise resource. You must test the entire ETL pipeline to ensure each type of data is transformed or copied as expected. Source data history, business rules, or audit information may no longer be available.Īdditionally, in the ETL process, data flows through a pipeline before reaching the data warehouse. The data source affects data quality, so data profiling and data cleaning must be ongoing. With data driving critical business decisions, testing the data warehouse data integration process is essential. This article will focus on the traditional ETL testing process. It also involves verifying data at each point between the source and destination. Data is extracted from the source, transformed to match the target schema, and loaded into the data warehouse.ĮTL testing ensures that the transformation of data from source to warehouse is accurate. ![]() Extract, Transform, and Load (ETL) is the common process used to load data from source systems to the data warehouse. The success of any on-premise or cloud data warehouse solution depends on the execution of valid test cases that identify issues related to data quality. As organizations develop, migrate, or consolidate data warehouses, they must employ best practices for data warehouse testing. ![]()
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