ETL Testing - Best Practices


To test a data warehouse system or a BI application, one needs to have a data-centric approach. ETL Testing best practices help to minimize the cost and time to perform the testing. It improves the quality of data to be loaded to the target system which generates high quality dashboards and reports for end-users.

We have listed here a few best practices that can be followed for ETL Testing −

Analyze the Data

It is extremely important to analyze the data to understand requirements in order to set up a correct data model. Spending time to understand the requirements and having a correct data model for the target system can reduce the ETL challenges. It is also important to study the source systems, data quality, and build correct data validation rules for ETL modules. An ETL strategy should be formulated based on the data structure of the source and the target systems.

Fix Bad Data in the Source System

End-users are normally aware of data issues, but they have no idea on how to fix them. It is important to find these errors and correct them before they reach the ETL system. A common way to resolve this is at the ETL execution time, but the best practice is to find the errors in the source system and take steps to rectify them at the source system level.

Find a Compatible ETL Tool

One of the common ETL best practices is to select a tool that is most compatible with the source and the target systems. The ETL tool’s capability to generate SQL scripts for the source and the target systems can reduce the processing time and resources. It allows one to process transformation anywhere within the environment that is most appropriate.

Monitor ETL Jobs

Another best practice during ETL implementation is scheduling, auditing, and monitoring of ETL jobs to ensure that the loads are performed as per expectation.

Integrate Incremental Data

Sometimes, data warehouse tables are larger in size and it is not possible to refresh them during every ETL cycle. Incremental loads ensure that only records changed since the last update are brought into the ETL process and it puts a huge impact on the scalability and the time taken to refresh the system.

Normally the source systems don’t have timestamps or a primary key to identify the changes easily. Such problems can be very costly, if identified at the later stages of the project. One of the ETL best practices is to cover such aspects in the initial source system study. This knowledge helps the ETL team to identify changed data capture problems and determine the most appropriate strategy.


It is best practice to make sure the offered ETL solution is scalable. At the time of implementation, one needs to ensure that ETL solution is scalable with the business requirement and its potential growth in future.