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Streamlining Your Data Pipeline Testing: How Automation Can Save You Time and Money

In today's digital-first world, where every click, swipe, and interaction translates into data, the stakes for data trust, governance, and quality have never been higher. For businesses and data professionals alike, navigating this vast ocean of information efficiently and ethically is not just a goal—it's a necessity. Missteps in handling data can lead to skewed analytics, misguided strategies, and, frankly, a whole lot of headaches. Beyond the immediate impacts, there's the long game to consider: reputational damage, legal entanglements, and lost revenue. It's a daunting scenario, but fear not; there's a roadmap to success.

That's where the principles of building robust data pipelines come into play, treating data engineering with the meticulous care of software development.

This approach isn't just about keeping our data pipelines running smoothly; it's about laying down a foundation of trust and reliability in the data we work so tirelessly to gather and analyze.

In this post, we'll dive into the essential guidelines for crafting data pipelines that stand the test of time—ensuring that your data isn't just plentiful, but meaningful, secure, and, above all, trustworthy. Stay tuned as we explore how to transform your data practices from good to great, one pipeline at a time.

OK, so what is the safest and easiest way to onboard data tests?

By sticking to the following guidelines when building data pipelines, and treating data engineering like software engineering, you can write well-factored, reliable, and robust pipelines.

Build an End-to-End Test of the Whole Pipeline

Don’t put any effort into what the pipeline does at this stage. Focus on infrastructure: how to provide known input, do a simple transform, and test that the output is as expected. Use a regular unit-testing framework like JUnit or pytest.

Use a Small Amount of Representative Data

It should be small enough that the test can run in a few minutes at most. Ideally, this data is from your real (production) system (but make sure it is anonymized).

Prefer Textual Data Formats over Binary

Data files should be different, so you can quickly see what’s happening when a test fails. You can check the input and expected outputs into version control and track changes over time.

If the pipeline accepts or produces only binary formats, consider adding support for text in the pipeline itself, or do the necessary conversion in the test.

Ensure That Tests Can Be Run Locally

Running tests locally makes debugging test failures as easy as possible. Use in-process versions of the systems you are using, like Apache Spark’s local mode or Apache HBase’s minicluster, to provide a self-contained local environment.

Minimize the use of cloud services in tests. They can provide a uniform environment but may add friction in terms of provisioning time, debuggability, and access (e.g., users have to provide their credentials for open-source projects). Run the tests under CI too, of course.

Make Tests Deterministic

Sometimes the order of output records doesn’t matter in your application. For testing, however, you may want an extra step to sort by a field to make the output stable.

Some algorithms use randomness—for example, a clustering algorithm to choose candidate neighbors. Setting a seed is standard practice, but may not help in a distributed setting where workers perform operations in a nondeterministic order. In this case, consider running that part of the test pipeline with a single worker, or seeding per data partition.

Avoid having variable time fields be a part of the output. This should be possible by providing fixed input; otherwise, consider mocking out time, or post-processing the output to strip out time fields. If all else fails, match outputs by a similarity measure rather than strict equality.

Make It Easy to Add More Tests

Parameterize by input file so you can run the same test on multiple inputs. Consider adding a switch that allows the test to record the output for a new edge case input, so you can eyeball it for correctness and add it as expected output.

So, as we turn data into our most strategic asset, remember: the journey to data excellence is ongoing, but with the right practices in place, every dataset becomes a stepping stone towards unlocking insights that propel us forward.


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