ETL Test Automation for Improved Business Processes
What is ETL Testing?
ETL, an acronym for Extract – Transform – Load, is a process that revolves around accurate data processing. It extracts data from sourcing systems, transforms information into a consistent data type (easy to read or understand) and then loads the data in a single repository. Now we all know how tiresome the process of managing data can be. Though research shows 98% of Fortune 500 companies use data to improve the customer experience, the US economy alone sheds roughly $3.1 Trillion per year because of poor data quality. Now that’s a huge number.
This is where ETL testing comes into the picture. ETL testing refers to the process of validating, verifying, and qualifying data while preventing duplicate records and data loss. It ensures the transfer of data from various heterogeneous environments to your central data repository. This happens under strict quality protocols and checks all necessary compliance regulations.
A developed ETL routine often includes additional features like error handling, logging infrastructure and 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; they 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 writeup, 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, analyse 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 are 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 focussed 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.
Such testing methods introduce ETL test automation at the very beginning of the testing cycle along with other automated methods at every possible stage of testing and development ultimately resulting in an updated and responsive ETL testing strategy.
1) Starting Point: Formal Modelling
As mentioned above, with the use of formal modelling all our testing or development related assets can be derived from the initial effort of mapping an ETL rule to a model. This formal model then becomes the checkpoint for future ETL validation.
Formal modelling helps to resolve the issue of ambiguity and incomplete test codes mentioned above. It maintains testability and help testers to quickly understand the logic that needs to be tested resulting in reduced time taken during the analysis stages. It can quickly analyse both the valid and invalid data that needs to be put in to fully test and analyse a transformation rule.
2) Test Cases Derivation from Flowchart Model
A major benefit of model-based testing is the fact that it can help automate the test case design.
The need to copy ghost code, or manually copy SQL from source to target database is eliminated and the path through the flowchart becomes the test case which is then used to pump data through the transformation rules. This derivation is almost impossible to implement when it comes to writing code from static requirements.
Here, automatic mathematical algorithms are applied to identify every possible path through the model, helping in generating test cases that cover every combination of input and output through homotopic analysis.
3) Automated Data Creation
Once your test case is coded, the next step for a tester is to check for data. This data can be fetched from the model or be created automatically or in some cases be drawn from multiple sources.
QA analysts use synthetic data generation engines like CA Test Data Manager that uses multiple ways to create the data required when using Model-Based Testing. This happens because, above functional logic, a flowchart can be laid with all required data. Simply put, as your ETL rules are modelled all your output names, variables and default values are defined. In situations when test cases are coded, data required to run those test cases can be auto-generated from given default values.
4) Automated Data Validation and Changes Implementation
Once your testers have everything, they need to test an ETL routine, the next step required is to automate the validation.
The greatest benefit of Model-Based Testing when it comes to ETL validation is the ability to respond to changes in high velocity. As the tests cases and data are linked so closely, a minor interference in the model is automatically reflected in the subsequent test cases and related data. This essentially means that with the ever-growing ETL routines, testing can scale up and not become a bottleneck in the Application Delivery pipeline.
Thanks to flowchart modelling, executing a change is similar to adding a new block to a flowchart.
Algorithms can then be checked to validate the model and ensure every piece of logic is well integrated with the main system. In the case of CA Agile Requirements Designer, Path Impact Analyzer can be used, to further spot the impact through your flowcharts. Defected test codes can then be fixed automatically, along with new tests added to achieve complete functional coverage that’s automatically generated.
Introducing ETL test automation is imperative for any organization striving for the Continuous Delivery of its software systems. A high degree of manual effort remains in ETL validation, from manually writing ghost code to sourcing the required data and comparing desired results. Model-Based Testing and intelligent Test Data Management can be effectively used to automate all of these tasks while allowing other teams to work from the same data sources in parallel development environments. we at ImpactQA follow a standardized automation approach to help enterprises speed up their testing processes to the next level. The presence of multiple environments, multiple user devices, and third-party integrations ensure better test coverage, high reusability and cost savings on a long-term basis.
Ensure reduced time-to-market for your next ETL testing project. Schedule a call and our experts will get in touch with you.