Why You Need Automated Testing in Your Data Engineering Projects
Every person or business working on data engineering projects should also implement efficient testing measures. This is because testing is a very important part when it comes to data engineering projects as it helps in validating the quality of the product being developed.
However, some business organizations, especially the small ones do not pay enough attention when it comes to testing their data engineering projects. This is despite the fact that testing plays a crucial role when delivering a project that meets all its requirements.
Even though the success of these projects is partly determined by having testing in place, it also depends on how the tests are run. Some companies invest in manual testing while others have thrown all their weight behind automated testing.
Companies should try as much as possible to eliminate or minimize manual testing and focus on automated tests due to the benefits they stand to reap. Some of these benefits include;
Offers Faster Feedback on Developed Features
It would take a lot of time to get feedback for features added to data engineering projects if companies were to solely rely on manual testing.
The use of automated test tools helps businesses reduce the time taken for them to get feedback on the performance of their data engineering projects.
This plays a crucial role in making validation fast especially for those whose engineering projects are worked on in phases.
With automated testing, businesses can detect any bugs and problems even before the projects are released, increasing the efficiency of the teams working on the projects.
Reduces Testing Expenses
The use of automated testing tools and automating all the testing procedures when working on data engineering projects reduces the expenses involved in testing.
This is because automation testing uses fewer resources compared to manual testing. However, businesses have to make sure that they have eliminated any manual testing procedures in their data engineering projects.
That notwithstanding, it is important to note that setting up and creating an automated testing environment for the first time will take a considerable amount of time as well as resources.
Even though it might be expensive, it gets cheaper to use the environment when testing data engineering projects in the long run.
It Saves Time
There are many processes and procedures involved when working on data engineering projects. One of the most important processes includes testing all the developed features.
With automated testing, the team working on the data engineering projects can spend less time testing the project since automated testing takes care of everything for them.
In addition, communication with other company departments such as product owners, design, and marketing is streamlined and improved especially because these departments depend on the test results when making decisions.
They can easily check the results themselves without having to talk with the development and data science teams.
Increases the Test Coverage
Using manual testing gets more difficult especially because it limits the number of tests that can be done on data engineering projects.
On the other hand, automated testing increases the test coverage since test teams can write as many tests as they want and add them to their testing suites.
This, in turn, allows companies to effectively test more features to make sure that their projects meet all their expectations.
This leads to the development of high-quality data engineering projects. In case of lengthy test cases that are often avoided when running manual testing, companies can use AI to automate and leave them running unattended.
When setting up your automated test suit for the first time, you are likely going to spend a considerable amount of money and go through several challenges.
This is the most difficult part when implementing test automation. However, once it is set up, everything else gets straightforward.
You can reuse your tests for all your future data engineering projects. You can even use the same test cases for different projects, something that shortens the time taken when testing data engineering projects.
In addition, even if you have to write new test cases, it gets quite easy because you have gained knowledge from the first test case scenarios that you have already written.
In conclusion, automating the testing of data engineering projects provides better insights especially when there are failed tests. Businesses can get insights on different things such as the internal state of the projects, file contents, data tables, and memory contents among others.