As organizations develop, migrate, or consolidate data warehouses, they must employ best practices for data warehouse testing. 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. Extract, Transform, and Load (ETL) is the common process used to load data from source systems to the data warehouse. Data is extracted from the source, transformed to match the target schema, and loaded into the data warehouse.
ETL testing ensures that the transformation of data from source to warehouse is accurate. It also involves verifying data at each point between the source and destination. This article will focus on the traditional ETL testing process.
The Importance of Data Warehouse Testing
With data driving critical business decisions, testing the data warehouse data integration process is essential. Data comes from numerous sources. The data source affects data quality, so data profiling and data cleaning must be ongoing. Source data history, business rules, or audit information may no longer be available.
Additionally, in the ETL process, data flows through a pipeline before reaching the data warehouse. You must test the entire ETL pipeline to ensure each type of data is transformed or copied as expected. Most importantly, the data warehouse is a strategic enterprise resource. Testing is required.
Understanding the ETL Testing Process
A solid understanding of data modeling provides testing teams with information to develop the right testing strategy. During the analysis phase, the testing team must learn and understand the different stages of the data warehouse implementation including but not limited to:
ETL testing includes multiple phases, and testing should be executed throughout the lifecycle of the data warehouse implementation, not just at the end.
Preparing for ETL Testing
A data warehouse implementation must include end-to-end testing. The QA team must test initial and incremental loads for the entire ETL process beginning with identifying source data to report and portal functions. 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.
With traditional ETL test planning, there are six phases:
Understanding business requirements/analysis
Creating test plans and estimating time to completion
Designing test cases and selecting test data
Executing tests with bug reporting and closure
Report summary and analysis
ETL testing is performed in five stages:
Identifying data sources and requirements
Implement business logic and Dimensional Modeling
Build and populate data