ETL, short for Extract, Transform, and Load, is the mantra that drives the world of data. Without ETL, valuable data would lie scattered across millions of digital sources and remain invaluable for further use. ETL testing consolidates the scattered data into a single repository, repurposes it for analysis, and facilitates decision-making processes.
If you are into ETL testing, this article is for you, since it explores beyond ETL testing introduction. Let’s go!
ETL testing is an approach to integrating data from diverse sources (Extract), transforming it into various styles and formats (Transform), and loading the newly transformed data into databases or warehouses for further use (Load).
When processing and migrating data to a new location, it is crucial to ensure that your data is of the highest quality. Here’s where ETL testing steps in. This process evaluates if your data:
ETL testing doesn’t end here. Data integration from disparate sources requires careful attention to detail. Despite best efforts, collated data is prone to errors that compromise data quality and accuracy. Here again, ETL testing helps identify and rectify mistakes such as:
During ETL testing, data is verified at several critical junctures to ensure accuracy and consistency. These checks allow data professionals to identify and correct issues early on and minimize the risk of costly errors.
Throughout the entire ETL process, the ETL tester is responsible for ensuring accurate data extraction, transfer, and loading into the new system. They are also responsible for conducting ETL testing after:
ETL testing is also conducted during data migration and if there are concerns regarding the data quality or ETL process performance. Effective ETL testing detects issues with source data before it's loaded into the repository and finds inconsistencies in business rules guiding data integration.
To understand ETL better, let’s discuss the stages of this crucial process in detail.
This initial stage involves designing the data model, defining the business flow, and evaluating the client’s expectations. These aspects lay the fundamentals that define the project scope, document it, and ensure that testers fully understand it.
In this stage, testers:
Testers create ETL mapping for various scenarios, write SQL scripts, and list the transformational rules. Each ETL mapping comprises:
Generally, this mapping is pre-approved by the head of Quality Analysis.
In this crucial stage, testers perform ETL tests according to the business requirements, identify bugs and defects, fix the issues, and draft and close reports before moving on to the next stage.
Testers execute data transformation to match the schema of the target data warehouse. They also check the data threshold and alignment to validate data flow. This ensures that each column and table matches the mapping document's data type.
Before and after migrating data to a warehouse, testers check the record count to confirm that all invalid data is removed and default values are accepted.
The Quality Analysis team prepares a r=summary report at the end of the ETL process. This report comprises:
Testers file and submit the ETL test closure report in this last and final stage.
The overall objective of ETL testing is to ensure error-free data transfer from the source to a target database or warehouse. The process itself is designed to address any challenges during the cycle. At different stages, various types of tests are conducted. Let’s analyze them in the following segment.
Error-free data is crucial for ETL testing since it guarantees accurate analysis reports. Production validation testing checks and validates the transferred data in the production system and compares it with the source data.
It verifies that data transferred to the target system has no loss or truncation and adheres to expected values after transformation.
However, the test doesn’t involve minute details like the value, order, and type of the data loaded.
Metadata is the type of data detailing the structure and data relationship. This test involves checks the following:
It also ensures that it adheres to the data and is used appropriately in the ETL process.
As the name implies, this test ensures that all the disparately sourced data is loaded into the target without duplication or loss.
This test checks if data has been consistently transformed according to multiple rules. It ensures that the data has been changed the same way every single time.
This test verifies the transferred data for accuracy, although the schema and data formats are different after transformation.
The objective of this test is to:
This testing involves:
This test checks the accuracy of the reported data. Testers rely on several calculations to assess the accuracy and verify the data layout and functionality as per the model.
When it comes to data, ETL plays a significant role in ensuring its accuracy and reliability, which are crucial in an organization’s data-based decision-making process. Our blog also highlights the importance of ETL testers and how their expertise makes a difference in ETL testing.
There will always be a constant need for ETL testers as long as data continues to exist. Join Aimore, the leading Software Training Institute in Chennai, and enrol in our top-notch ETL testing courses in Chennai now. Grab the opportunity and make the most of it to lay the foundation for a bright career. Contact us for further details.