Now that more organizations are using ETL tools and processes to integrate and migrate their data, the obvious next step is learning more about ETL testing to confirm that these processes are operating as expected. ETL testing can be challenging simply because of the volume of data involved. Plus, the data is almost always heterogeneous, which adds another layer of complexity.
However, ETL testing is important because often, the data that’s being moved to target databases or data warehouses will ultimately be used for data analytics and business intelligence purposes. And if you’re going to rely on data to inform your business decisions, it’s important to make sure that the data is complete and accurate.
What is ETL Testing?
ETL testing confirms that the data we have extracted, transformed, and loaded has been extracted completely, transferred properly, and loaded into the new system in the correct format. Testing can also help identify and prevent issues with data quality during the ETL process, such as duplicate data or data loss.
Testing also confirms that the ETL process itself is running smoothly and that there are no bottlenecks. We want to make sure that the ETL process doesn’t suffer from performance issues that might impact the performance of either the source or destination systems.
When do we need ETL Testing?
ETL testing can be helpful:
- When setting up a data warehouse for the first time, after data is loaded
- After adding a new data source to your existing data warehouse
- After a data integration project
- After a data migration project
- When moving data for any reason
- If there are suspected issues with data quality in any of the source systems or the target system
- If there are suspected issues with the performance of ETL processes
What are the 8 stages of the ETL testing process?
We’ve identified 8 stages of effective ETL testing:
- Identify your business requirements. Business requirements include defining data sources, the target system, and the level of transformation required between them. Look at the existing data model and update it if necessary.
- Assess your data sources. Perform a count of records of the primary source table so that you can later confirm that all of the data was moved.
- Create test cases. Consider testing the data on the target system for completeness and quality. Performance testing of the ETL process is also a good idea.
- Begin the ETL process with the extraction. Extract data from the source systems. Confirm that all of the data has been extracted properly and completely.
- Perform the necessary data transformation. Confirm that the data has been transformed to the appropriate format for the target system and that it complies with business rules.
- Load the data into the target destination. Check the record count to confirm that all of the data has been moved. Confirm data integrity by checking to see if any records were rejected by the target system and not loaded.
- Document your findings. Document any bugs or issues that you’ve identified during the test process. If necessary, adjust your processes to address the issues and repeat steps 4 through 6 again.
- Conclude testing and proceed with ETL.
What are the challenges?
The testing process can be challenging simply because of the volume of data that’s often involved. ETL testing is also very different from typical software testing because it’s primarily about data, not code. The data type can be different for the data coming from each source, so the testing must accommodate heterogeneous data types. Plus, the data is often in a different format in the source and target systems, so testing the data in the different locations can feel kind of like comparing apples to oranges.
How to get started
The first step getting started is to determine what methods or tools are right for your organization. Many organizations use SQL scripting to test their ETL processes. Doing your own scripting may save money in the short term, but could potentially take longer than using an existing tool that supports ETL testing. It’s important to work with all of the relevant stakeholders before making decisions about how to proceed. A kick-off meeting with key stakeholders can help you determine the best course of action.
Want to learn more?
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Matillion ETL software is cloud-native, purpose-built to support leading cloud data warehouses, including Snowflake, Amazon Redshift, Google BigQuery, and Microsoft Azure Synapse, as well as Delta Lake on Databricks. From extracting and loading your data to performing powerful data transformations, Matillion cloud ETL solutions offer cloud-native architecture and performance compared to legacy ETL tools. Matillion leverages the performance and scale of the cloud, making complex data transformation fast, secure, and cost efficient.
Request a demo to learn more about how you can unlock the potential of your data with Matillion’s cloud-based approach to data transformation.