ETL Testing: Proud Tradition or Popular Innovation?

ETL Testing

With the vast amounts of data, modern businesses rely on in their operations, ETL testing strategies appear to be more than just a necessity. This article explains the process of ETL testing, its types and challenges and clarifies the parameters companies should consider when opting for a good ETL testing tool.

ETL Testing is derived from the original ETL process. It stands for Extract, Transform and Load and is the primary approach Data Extraction Tools and Business Intelligence (BI) Tools use to extract data from a data source, transform that data into a common format that is suited for further analysis, and then load that data into a common storage location, normally a Data Warehouse. Basically, ETL testing is a data-centric testing process to validate that the data has been transformed and loaded into the target as expected.

Organizations with organized IT practices are looking forward to creating the next level of technology transformation. They are now trying to make themselves much more operational with easy-to-interoperate data. Here are a few situations where ETL Testing can come in handy:

  • Following a data integration project.
  • Following a data migration project.
  • When setting up a data warehouse for the first time, after the data gets loaded.
  • After adding a new data source to your current data warehouse.
  • When migrating data for any reason.
  • In case there are any suspected issues with the performance of ETL processes.
  • If there are any suspected issues with data quality in any of the source systems or the target system.

Similar to other testing processes, ETL also goes through different steps. We’ve identified 8 steps 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.
  • Begin the ETL testing 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.

ETL testing is different from application or conventional testing because it requires a data-centric testing approach. Although ETL Testing is a very important process, there can be some challenges that companies can face when trying to deploy it in their applications. Sharing the list of most common ETL testing challenges below:

  1. Frequent changes in the customer’s requirements cause re-iteration of test cases and execution.
  2. Failure to understand business requirements or when employees are unclear of the business needs can cause issues.
  3. Due to changes in requirements by the customer, a tester might need to re-create/modify mapping documents and SQL scripts, which leads to a slow process.
  4. Data loss can occur during migration because it is hard to perform source-to-target reconciliation.
  5. Change in the data source or incomplete/corrupt source data can be an issue
  6. Real-time data may impact the reconciliation process between data sources and target destinations.
  7. The huge volume of historical data may cause memory issues in the system.
  8. Using inappropriate testing tools can cause problems
  9. Having an unstable testing environment can also create problems

Not all the tools can be applied to every user’s needs. Every testing team has different requirements, and thus it is essential to choose the ETL testing tool to avoid future bottlenecks. Some of the parameters to consider when choosing an ETL testing tool are below.

  • Cloud Compatibility: The ETL testing tool should have built-in compatibility with Cloud systems such as Amazon Web Services, Google Cloud Platform, Salesforce, Snowflake and more.
  • User Interface: A good UI solves many problems.
  • Built-in Connectors: An ETL testing tool should have built-in connectors to connect source and target datasets to carry out testing. It should also have the capability to read different file formats like CSV, XML, JSON, etc.
  • Automatic Code Generation: A good testing tool must have Automatic Code Generation ability.

The use of special test automation tools, such as iCEDQ, Datagaps ETL Validator, Talend Data Integration, etc., saves a ton of time and allows you to replace some labor-intensive processes. Automation software can be limiting and expensive, but incorporating at least some will result in better efficiency.

ETL testing is designed to ensure that the data loaded from a source to a destination after transformation is accurate. It involves the verification of data at various stages, which is used between source and destination. Let’s find out the main ETL Testing scenarios:

  1. Production Validation. It is a table reconciliation or product balancing technique, which usually validates the data in the target systems.
  2. Source to Target Testing. In this ETL testing scenario, the count from source systems is tallied to check if it matches the target system’s expected records.
  3. Metadata Testing. Data warehouse table definitions verification, including data type check, data length check and constraints testing.
  4. Performance Testing tests the systems’ performance, which determines whether data is loaded within expected time frames to the systems and how it behaves when multiple users log onto the same system.
  5. Data Transformation Testing, where SQL queries are run to validate business transformations.

Several other ETL testing scenarios can also be performed at this time to ensure the best quality, such as Data Quality Testing, Data Integration Testing, Report Testing, Application Migration Testing, Duplicate Data Check and Source to Target Count Testing.

During the ETL testing process, one must constantly check how the whole pipeline works and whether data remains consistent with its mapping instructions. Although there are slight variations in the type of tests that need to be executed for each project, below are the most common ETL testing types that need to be done: data quality and completeness, metadata, data transformation process compliance, performance and integration testing.

Businesses today rely heavily on data, which is a good thing! When data is subjective and accurate, it gives us a better understanding. With the help of ETL testing, you can check that the data safely traveled from its source to its destination and guarantee its high quality before it enters your Business Intelligence reports.

Overall, testing plays an important part in governing the ETL process, and every type of company must incorporate it into their business.

Cprime provides QA services. Not sure about the Quality Assurance and don’t know whether you require a specialist or not? We can help. Need the whole QA team? Cprime is ready to manage the staff augmentation. Contact us to get more information about the services we offer.