Open-Source Data Observability with Elementary — From Zero to Hero (Part 2)

Open-Source Data Observability with Elementary — From Zero to Hero (Part 2)

The guide to take your dbt tests to the next level for free

In the previous part, we have set up Elementary in our dbt repository and hopefully also run it on our production. In this part, we will go more in detail and examine the available tests in Elementary with examples and explain which tests are more suitable for which kind of data scenarios.

Here is the first part if you missed it:

using dbt’s building blocks. It powers up your testing coverage quite a lot, but that part requires its own article.

How are we using the tests mentioned in this article? Besides some of the tests from Elementary, we use Elementary to write metadata for each dbt execution into BigQuery so that it becomes easier available since these are otherwise just  ! Let me know if you have any questions or suggestions.

References In This Article

• dbt Labs. (n.d.). run-results.json (Version 1.0). Retrieved September 5, 2024, from
• Elementary Data Documentation. (n.d.). Elementary Data Documentation. Retrieved September 5, 2024, from
• Elementary Data. (n.d.). GitHub Repository. Retrieved September 5, 2024, from was originally published in Towards Data Science on Medium, where people are continuing the conversation by highlighting and responding to this story.
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