ETL Testing Tool

ETL processes are tested and certified before their deployment into production. However, because of large data volume, it is really cost-prohibitive to test them manually. Hence, we developed ETL test automation tool, iceDQ. This article discusses ETL testing tools, their purpose, and their benefits.

For underlying concepts, please refer to prior article ETL Testing Concepts.

What is ETL Testing Tool?

ETL Testing Tool is a purpose-built software designed to automate testing and certification of the ETL processes.

ETL test automation suite supports creation of test cases, physical tests, execution of the tests, and reporting of the test results inside the tool or integrated with test case management tools. ETL testing software is used in non-production environments.

End-to-End ETL Testing Automation Tool

An End-to-End ETL test automation tool needs to automate plus support the complete data development lifecycle. Many believe that once the testing is done and the code is deployed in production that is the end of the testing. That might be true for the software life cycle but is absolutely not true for data.

End-to-end testing spans the development and embedding of some of the test automation in production, also called Whitebox testing. This way both left-shift and right-shift approaches are covered.

On the left side of the testing involves complete automation and capture of test cases, tests, and integration with DevOps so that the manager gets a complete auditable test result in their hand.

On the right side of the deployment, you are in production. At this point, the operations teams can use the knowledge gained by the automation team and proactively borrow and embed the test to monitor known data traps.

At some point, new data is discovered or even the business changes might require redevelopment and regression tests. The ETL Testing software must be able to support the testing of changes by supplying some old and new rules to certify the changes.

1. Exponential increase in data volume: The ETL testing software is also unique as it needs to be scalable to test billions of records. Comparing large volumes of data by simply eyeballing is highly prone to error. A manual inspection process also forces the user to test a smaller sample for ETL Testing. This can result in missing critical and essential ETL testing scenarios.
2. Increase in the number of ETL processes: There are thousands of ETL processes that are under development, deployed, or enhancement. It is very difficult to test, track and report the validation of the ETL.
3. Complexity of the ETL process: ETL testing tools cannot directly test the ETL processes. Instead, ETL testing tools validate the data generated by the ETL, and if the data is valid, only the ETL process is certified.
   The data generated by the ETL is dependent on the input data and the transformations. Hence, the ETL test automation platform must have the ability to connect both the data source and destination database or files to compare and validate the data.
4. Iterative change testing: ETL transformations keep changing based on new business requirements. Hence, regression ETL testing becomes extremely complex and costly in the absence of automation testing. You need to recall the old tests to combine them with the new ones and certify the ETL tool.
5. Digitization and Automation requirements: While there is a growth in the data requirements, there is also a lowering of budget and project timelines. Digitization and Automation are the only way forward.
6. UI-based application testing software cannot test ETL process: There are many types of software. On one side you have mobile and web applications on the other side you have data integration or ETL processes. Even though test automation software they were never designed to test ETL process.
7. DevOps Integration: Creating CICD pipelines need automated ETL testing tools. As companies are moving towards fully automated deployment so is the need for ETL testing tools that can integrate with the automated releases.
8. Reduction of data issues in production: Catching data issues early before they go into production is essential. The data process and orchestration issues can only be found during testing.

ETL Testing Automation using Selenium

Software developers with testing backgrounds often wonder, how is ETL testing tool such as iceDQ different from Selenium. Can I use Selenium for ETL testing? Short answer “NO”. If you visit selenium.dev, on their front page it clearly says, ”Selenium automates browsers. That’s it!” So, you need a screen to use it.

In contrast, ETL processes run in the background and there is no Graphical User Interface available to test. Hence, ETL Testing requires the verification of input data and output data of an ETL process based on various types of rules.

Best ETL Testing Tools List

Surprisingly there are very few ETL tests automation suite like iceDQ. Here’s the list of features that define the best ETL testing tools. Use the following list of parameters to evaluate the best ETL testing tools.

1. User Workflow Integrations
2. Enterprise Ready Architecture
3. Integrations
4. Rules Engine
5. Data Life Cycle Support
6. Connectors
7. GUI
8. DevOps Support

Please refer iceDQ features for more details.

ETL Testing Tool Free Download

A firsthand experience is essential to understand the usage of ETL testing tool. Download the ETL testing tool for free.

Open Source ETL Automation Testing Tools

ETL Automation Testing Framework

While ETL test Automation is important you will need some kind of framework consisting of both practices and ETL testing tools to improve QA professionals’ productivity and effectiveness. With iceDQ software, we recommend a data-driven + rules-based testing framework. We will divide the framework into two primary components process and tools.

A. Process

• Senior management must create regulations that prevent any ETL process from deployment in production must be accompanied by a test and a passing result.
• All testers must be provided access to complete data, obfuscated where necessary. NO data sampling should be allowed.
• The business user must be integrated with the testing team so that the test requirements are captured by the QA analyst in parallel to the data requirement and data mapping exercise.
• The tester must create the physical tests that should be linked to QA requirements and tagged with data assets such as tables and ETL processes.
• The execution of the test must be scheduled and/or integrated into a DataOps pipeline.
• Reporting should be provided to ensure test coverage and compliance.

B. Tool

• The ETL test automation software such as iceDQ must support all the above processes defined in the testing framework. Some of the items are the ability to process large volumes of data, data connectivity, integration with requirements, test management suites, and reporting.

ETL Testing Automation using Python

What about in-house development with Python? ETL Test automation using Python is a repeatable orchestration and execution of ETL tests using Python scripts with minimum human intervention in the quality assurance process. There are two components of ETL testing automation with Python.

1. The actual automated tests that verify the processes.
2. The orchestrating and executing the test.

It must be clear that Python has a very little role if any in the actual creation of the tests. However, for orchestration and execution of the test absolutely. Imagine a scenario you must add some test data, execute a procedure then run test an ETL test and at the end remove the data and drop all the tables and delete files. This all can be done with Python programs. Python however is not a replacement for ETL testing tools in any way, shape, or form.

ETL Performance Testing Tools

ETL performance testing is measured in the form of time required for the ETL process to start, to end, and the number of records processed. Performance testing, however, does not include any of this to verify if the data process transformed or loaded the data correctly.

Usually performance testing = # of rows processes/ (end time – start time)

When the ETL process starts, it logs the start time in a log table. When the process ends, it logs the end time and the number of rows of processes. Once the ETL process is executed, the ETL performance testing rules collect the data and calculate the rows per second.

The Performance of ETL process depends on the following factors:

1. The data volume.
2. The time is taken by the process to complete.
3. The number of updates, deletes, and inserts.
4. Usage of database logs or bulk processing.
5. The ETL server specifications.
6. The Database server specifications.
7. The efficiency of codes.
8. The usage of database index and other mechanism.

However, purely measuring performance might not be enough as sometimes the ETA might not be achieved because the upstream processes are late. Any ETL testing tool should be able to provide the performance numbers and watch for the ETA.

Is ETL Testing Manual or Automated

ETL testing is not manual. It is a continuous process of thinking about requirements, capturing the requirements, and creating test cases. You may consider analyzing and thinking as a manual process. But there is no software in the world that can do the thinking for you.

The next step is creation of tests in product like iceDQ or using RuleGen utility to create rules. Once the rules are in place. Automation takes over where various APIs the execution part is 100% automated. The reporting of the test results and feedback into test case management tools also is completely automated.

ETL Testing Automation Ideas

It all starts with the ETL test automation tool, but your testing is as good as the data used by the tool. Don’t do sampling. Use all data. Just because you are in the development or QA area doesn’t mean you use development data. Instead, we want you to use production data as much as possible.

There are many ideas and methods to automate ETL testing, but as defined earlier it starts with a framework that includes both the process and the ETL testing tool. You will essentially need to go through the ETL testing concepts to get a detailed account.

For the ETL automation testing tutorial, please refer to our resources section where you will find all kinds of tutorials for ETL automation testing.

As organizations learn to deal with an increasing amount of data volume and the complexity of ETL processes, they are finding it challenging to automate ETL testing, increase test coverage, and reduce data issues at the same speed. To solve the above challenges, you must choose the right ETL testing tool and enable your users to perform testing efficiently.

iceDQ your ETL Testing Tool

• Compare 100% of your data using our in-memory ETL testing engine and get the best performance. Read more
• Connect to any heterogeneous data source, be it a database, file, or an API to perform ETL Testing effectively. Find the complete list of connectors here. Read more
• Integrate with test cases management tools like HP ALM, defect logging tools like Jira, and CICD tools like Jenkins using our Rest API and CLI to automate your complete data pipeline.
• Schedule ETL Testing rules using our inbuilt scheduler. If required, you can trigger the tests using an external scheduler too. You can read all about the iceDQ features here. Read more