Extract, Transform, and Load (ETL) is a data processing technique that concentrates on accuracy. It collects data from source systems, converts it into a standardized data type (simple to read or comprehend), and then stores it in a single repository. We’ve all experienced how exhausting data management can be. Despite the fact that 98 percent of Fortune 500 firms utilize data to improve the customer experience, poor data quality costs the US economy $3.1 trillion every year. That’s a large sum of money!
ETL testing comes into play in this scenario. The process of validating, confirming, and certifying data while avoiding duplicate records and data loss is referred to as ETL testing. It guarantees that data from a variety of heterogeneous contexts are sent to your central data repository. This is done in accordance with high-quality standards, and all relevant compliance rules are checked.
A developed ETL routine often includes additional features like error handling, logging infrastructure, and a routine environment. Mostly, ETL is used to prepare large and disparate data for analytics and business intelligence. Additionally, data migration for new systems, along with handling data integrations, sorts, and joins; all fall under the various benefits of the ETL process.
The issue that arises here is the fact that despite the increased use of ETL in data management systems, comprehensive ETL testing reflects how slow and manual the entire process of testing is. It shows how an unacceptably high number of defects are pushed onto the production environment without proper quality checks.
In this write-up, we’ll talk about the various challenges faced in ETL testing and how ETL test automation can come as a preferred solution to the listed problems.
Common Challenges to ETL Testing
Usually, manual validation is used to test how successful data migration has been while using ETL. The process typically creates a shadow code set and uses it to transform data. The next step is to compare actual results to the expected ones to validate that the data has been correctly transformed.
1) The Root Cause: High Complexity
The major issue that comes in such manual validation is the fact that ETL routines become highly complex as the size of your business grows. The variety and volume of data that is collected push the ETL rules to grow; a growth, our traditional testing methods are yet unable to scale up to. The complexity of the systems designed to extract, transfer, analyze and present this data is growing exponentially as well. The various factors that may impact the complexity of these transformations include:
- The number and variety of data sources involved, data targets, and complex transformations
- The number of re-usable transformations, code snippets, and joins
- The number of tables created
2) Unsystematic Documentation
The increased complexity impacts the testability of ETL routines to a great extent. Unclear documentation of transformation rules is the major cause behind the same. The rules here are mostly penned during the development phases and frequently stored in written documents or spreadsheets, or worst- never stored at all. Such incomplete or ambiguous documentation meant that testers had no way to easily or accurately understand the ETL routines. As a result, testers are often left filling blanks resulting in invalid data being copied to the target.
3) Compromised Quality
As noted above, with bad documentation, manual derivation becomes the way to go which is highly unsystematic and results in vague test cases. Ultimately, it’s difficult for even the most talented and certified testers to create an accurate testing strategy to validate all possible data combinations and potential failures.
It leads to massive under-testing or over-testing where only a fraction of the possible logical outcomes is tested.
Such poor coverage results in codes full of defects that are expensive and time-consuming and mostly go undetected. On the other hand, testers have no reliable way to measure the coverage of their test cases either.
4) Time and Effort
Comparatively testing the individual fields to expected results is a time-consuming job. Given the amount of data produced by a complex ETL routine and the fact that the source data will often be stored in a diverse variety of database and file types, it is also difficult, as the transformed data needs to be validated on multiple levels.
The various checkpoints include:
- Data accuracy
- Data consistency
- No duplicities
Alternative: ETL Test Automation
It’s evident from the above challenges, as long as test cases are pulled manually and compared, ETL testing will never be able to match with the increasing market demands for data security and quality checkpoints.
An alternative strategy is focused more on model-based and requirement-based testing methods. It’s designed in a manner that shifts the testing efforts to the left as opposed to the waterfall model. There are various benefits of choosing a shift-left testing strategy. As a result, you get a quality product right from the beginning of the project.
ETL test automation is introduced from the start of the testing cycle, along with other automated approaches at every feasible level of testing and development, resulting in a dynamic and adaptive ETL testing strategy.
To get a better viewpoint regarding the ETL testing strategy, refer to the blog mentioned below.
Originally published at https://www.impactqa.com on November 9, 2021.
