Automating ETL Testing

Once ETL is complete, it’s important to test the final dataset to ensure the process has been successful, and that it meets your specifications. But if your data pipeline includes large data sets or multiple sources, conducting ETL testing manually is extremely time-consuming, difficult and prone to mistakes. This is where you need automation to take the strain and insure reliability.

How to automate ETL testing

Here is an example of how to build an automated ETL testing mechanism:

1. Choose your ETL testing automation tool – This could be a ready-made platform, or a manual programming language with powerful database drivers, such as Selenium or Java with JDBC (Java DataBase Connectivity).
2. Create a workflow model – The process of developing an ETL workflow is often ad hoc, and based on trial and error. While it requires a certain time investment, formal modeling of your workflow can prevent or mitigate many issues which can occur further down the line. This could be in the form of a flowchart, following the logical stages of the data pipeline from beginning to end. However, it’s important to remember to create a workflow model before starting your ETL process.
3. Derive test cases from your model – Since each of the stages in the flowchart are ‘If, then’ statements, you can easily transform each path through the workflow into a test case. You can do this automatically, generating test cases which use every combination of inputs and outputs, and covering every possible path through the model.
4. Generate the data required for the test cases – Once you’ve created all of the test cases, you’ll need to apply data sets which cover all of them. A data generation engine can produce synthetic data required for testing, again using your workflow model. This is because ETL rules can easily define output names, variables, and default values for each node.
5. Create a test mart – You must match the correct data to the correct test case, which you can do using a ‘test mart’. Here, you can assign records, which match certain criteria, to each test. Test data is usually stored in a test data warehouse.
6. Execute the tests – Now you’ll need to run the tests and evaluate the results. Each test case should output a ‘pass’ or ‘fail’ result, based on your pre-defined specifications.

Which automation tool is best for ETL testing?

ETL automation means dealing with increasingly large and disparate data sets using predefined rules and the speed advantage of computers. As with any software, selecting the best ETL automation tool depends on your requirements. There are many tools out there, such as RightData, Integrate.io, and iCEDQ.

Plus, you may have several different ETL testing automation ideas which you’d like to try. Here are some general guidelines you can follow to find the software that’s right for you:

• Licensing and requirements – What is the license model? Is there a free trial available, and for how long? Does the tool support in-house deployment? What are the hardware requirements? Does the tool provide a web portal?
• Data sources – Which relational databases are supported, e.g., SQL Server? Which cloud technologies, e.g., Azure, BigQuery, Snowflake? What about flat file data sources, e.g., XML and spreadsheets?
• Comparing the source to the target – Can the tool perform metadata validation between the source and the target? Does the tool support transformation testing? What is the maximum number of records that can be applied to a single test case, and is there a limit on the number of columns? Does the tool support incremental ETL processes?
• Data quality testing – Does the tool support data profiling and comparison of profiles? Does it support testing data quality rules for different dimensions, e.g., accuracy and completeness?
• Automation – Can the tool schedule test case runs? Does the tool support version control of test scripts, e.g., using GIT? Is there an API for creating and running test cases? Can test cases be copied from one workflow to another?
• Reporting – Can the data differences be exported to a spreadsheet? Can a ‘column differences’ report be generated to easily identify issues? Does the tool support custom reports?