Unit testing dbt fashions has all the time been one of the vital important lacking items of the dbt ecosystem. This text proposes a brand new unit testing method that depends on requirements and dbt greatest practices
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Ever since dbt launched software program engineering greatest practices to the realm of knowledge engineering, its functionalities and the ecosystem round it have saved increasing to cowl but extra areas of the info transformation area.
Nevertheless, one important piece of the “knowledge engineering with software program engineering greatest practices” puzzle stays elusive and an unsolved drawback: unit testing.
Justifying the significance of unit exams, why they’re important for any line of code earlier than it may be known as “production-ready”, and why they’re completely different from dbt Exams or knowledge high quality exams is one thing that has already been brilliantly tackled and defined. But when we needed to summarize their significance in a one-minute elevator pitch, it’d be the next:
In knowledge engineering there are typically two completely different parts that we need to take a look at: the info and our code — dbt Exams (and different knowledge high quality techniques/instruments) permit us to check the info, whereas unit exams permit us to check our code.
With the above in thoughts, it’s solely pure that there have been a number of initiatives by the neighborhood to boost dbt with an open-source unit testing functionality (like Equal Specialists’ dbt Unit Testing package deal or GoDataDriven’s dbt-focused Pytest plugin). Nevertheless, these packages stay restricted in functionalities and have a steep studying curve.
This text introduces a special method that’s a lot easier but extra elegant, counting on requirements and dbt greatest practices to implement a scalable and dependable unit testing course of.
Earlier than diving into the method, let’s first outline the extent at which we need to run our unit exams. The query to reply…