What are some best practices for testing ETL and ELT processes in dimensional modeling?

In my point of view, some practices are recommended, such as: Clearly understand the business requirements and expected results of ETL/ELT processes, Perform data profiling to understand data characteristics, identify anomalies and assess data quality, Conduct unit tests for individual components of the ETL/ELT process, Perform integration tests to validate interactions between different components of the ETL/ELT process, Implement regression tests to ensure that new changes or improvements do not introduce problems to existing functionality, Implement data reconciliation to compare source and target data at various stages of the ETL/ELT process and Perform performance testing to evaluate the scalability and efficiency of ETL/ELT processes.

Una buena practica es aplicar la tecnicas de exploración de datos, dado que entender la naturaleza de los datos es importantísima para no tardar tanto en resolver errores en las pruebas de los procesos ETL/ELT.

To ensure robust testing of ETL and ELT processes in dimensional modeling, consider the following best practices: Develop comprehensive test cases covering various scenarios and edge cases. Utilize sample datasets representative of real-world data to validate transformations. Implement automated testing frameworks to streamline regression testing. Verify data integrity and consistency across dimensions and fact tables. Conduct performance testing to assess scalability and optimize processing times.

In my experience, testing source data before transforming it is crucial. We use SQL queries, data profiling, and quality tools to ensure data completeness, validity, uniqueness, integrity, and timeliness. SQL queries help verify counts, null values, and compare data. Data profiling provides insights into data structure, content, and quality. Quality tools detect and resolve errors like duplicates, ensuring clean data for accurate analysis.

In my years of building data pipelines, rigorous testing of ETL/ELT processes has been key. Best practices include: Automate Testing: Leverage unit testing frameworks for individual transformation logic. This ensures each piece works as expected in isolation. Data Comparison: comparing source vs. target, validating mappings, and ensuring data integrity throughout the flow. End-to-End Testing: Simulate the full ETL/ELT process, including edge cases, to catch integration issues. Continuous Integration: Incorporate testing into CI/CD pipelines for immediate feedback on changes. Remember, comprehensive documentation and maintaining a test data repository are also crucial for efficient testing and troubleshooting.

In my experience in an e-commerce environment, testing transformations is crucial for ensuring accurate data processing. We validate business logic, integrity, and consistency, such as verifying revenue calculations align with sales quantities and prices. Automated tools aid in comparing transformed data against expected results, ensuring data accuracy and reliability.

Below are the best practices I follow generally 1. Ensure there is no data duplication based on the source keys , if you are using surrogate keys as primary key 2. Grain of all tables are correct and as per requirement 3. In the fact tables , the foreign keys (dimension keys ) are populated correctly so that the dimension - fact join works correctly. 4. Data and joins between dimension and fact table is able to provide the reporting columns and adheres to reporting requirements.

First, don't go by technical STTM. Testing should be based of the requirements. How the data should be handled in the consumption layer? What should be the grain of the data? What sort of the quality requirements my data should meet? Considering the overall pipeline itself, what are my performance requirements, automated triggering, archival, purging requirements? These are just a few, but once identified, all of these requirements should go into the automated testing tool that tests all the scenarios before the code movement to the higher environment.

This article is missing a pretty important step 0: sit down and think about what the model should look like before you write a single line. Sketch out an ERD - like, physically, on a piece of paper. Ask yourself what's missing. Ask yourself what columns, tables, or transformations you don't need. Ask others what they want to have on their dashboards. What questions they answer. How they want to use the data.