ADadeelk93·Feb 1, 2023Recommendation phrase
And if you want/need to take it further, I love this package: https://github.com/calogica/dbt-expectations
In Ask HN: How do you test SQL?calogica
dbt-expectations
Shell2021–2023
calogica / dbt-expectations
dbt-expectations
Port(ish) of Great Expectations to dbt test macros
3Distinct people
3Mentions
1Explicit recommenders
1.2KGitHub stars
What to know before exploring
What it is
Port(ish) of Great Expectations to dbt test macros
GitHub
Where it is used
No documented product use has been verified yet.
What people are saying
And if you want/need to take it further, I love this package: https://github.com/calogica/dbt-expectations
adeelk93 used a recommendation phrase. One indexed comment matches. This is a phrase match, not a verdict.
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Also in these discussions
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This week on Hacker News
Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 5:04:57 PM UTC.
No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.
Mentions over time
HN mentions by month3 dated mentions
Observed mentions: 2021-10-09 08:04:44 UTC to 2023-02-01 15:36:06 UTC.
Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.
View mention counts and date ranges
| Period | Start (UTC) | End (exclusive, UTC) | Mentions | Submissions | Comments |
|---|---|---|---|---|---|
| Oct 2021 | 2021-10-01 00:00:00 UTC | 2021-11-01 00:00:00 UTC | 1 | 0 | 1 |
| Nov 2021 | 2021-11-01 00:00:00 UTC | 2021-12-01 00:00:00 UTC | 0 | 0 | 0 |
| Dec 2021 | 2021-12-01 00:00:00 UTC | 2022-01-01 00:00:00 UTC | 0 | 0 | 0 |
| Jan 2022 | 2022-01-01 00:00:00 UTC | 2022-02-01 00:00:00 UTC | 0 | 0 | 0 |
| Feb 2022 | 2022-02-01 00:00:00 UTC | 2022-03-01 00:00:00 UTC | 0 | 0 | 0 |
| Mar 2022 | 2022-03-01 00:00:00 UTC | 2022-04-01 00:00:00 UTC | 0 | 0 | 0 |
| Apr 2022 | 2022-04-01 00:00:00 UTC | 2022-05-01 00:00:00 UTC | 0 | 0 | 0 |
| May 2022 | 2022-05-01 00:00:00 UTC | 2022-06-01 00:00:00 UTC | 0 | 0 | 0 |
| Jun 2022 | 2022-06-01 00:00:00 UTC | 2022-07-01 00:00:00 UTC | 0 | 0 | 0 |
| Jul 2022 | 2022-07-01 00:00:00 UTC | 2022-08-01 00:00:00 UTC | 0 | 0 | 0 |
| Aug 2022 | 2022-08-01 00:00:00 UTC | 2022-09-01 00:00:00 UTC | 0 | 0 | 0 |
| Sep 2022 | 2022-09-01 00:00:00 UTC | 2022-10-01 00:00:00 UTC | 0 | 0 | 0 |
| Oct 2022 | 2022-10-01 00:00:00 UTC | 2022-11-01 00:00:00 UTC | 0 | 0 | 0 |
| Nov 2022 | 2022-11-01 00:00:00 UTC | 2022-12-01 00:00:00 UTC | 0 | 0 | 0 |
| Dec 2022 | 2022-12-01 00:00:00 UTC | 2023-01-01 00:00:00 UTC | 0 | 0 | 0 |
| Jan 2023 | 2023-01-01 00:00:00 UTC | 2023-02-01 00:00:00 UTC | 0 | 0 | 0 |
| Feb 2023 | 2023-02-01 00:00:00 UTC | 2023-03-01 00:00:00 UTC | 2 | 0 | 2 |
What Hacker News says
3 sourcesI did data engineering for 6 years and am building a company to automate SQL validation for dbt users.
First, by “testing SQL pipelines”, I assume you mean testing changes to SQL code as part of the development workflow? (vs. monitoring pipelines in production for failures / anomalies).
If so:
1 – assertions. dbt comes with a solid built-in testing framework [1] for expressing assertions such as “this column should have values in the list [A,B,C]” as well checking referential integrity, uniqueness, nulls, etc. There are more advanced packages on top of dbt tests [2]. The problem with assertion testing in general though is that for a moderately complex data pipeline, it’s infeasible to achieve test coverage that would cover most possible failure scenarios.
2 – data diff: for every change to SQL, know exactly how the code change affects the
Read the rest
output data by comparing the data in dev/staging (built off the dev branch code) with the data in production (built off the main branch). We built an open-source tool for that: https://github.com/datafold/data-diff, and we are adding an integration with dbt soon which will make diffing as part of dbt development workflow one command away [2]
We make money by selling a Cloud solution for teams that integrates data diff into Github/Gitlab CI and automatically diffs every pull request to tell you the how a change to SQL affects the target table you changed, downstream tables and dependent BI tools (video demo: [3])
I’ve also written about why reliable change management is so important for data engineering and what are key best practices to implement [4]
[1] https://docs.getdbt.com/docs/build/tests
[2] https://github.com/calogica/dbt-expectations
[3] https://github.com/datafold/data-diff/pull/364
[4] https://www.datafold.com/dbt
[5] https://www.datafold.com/blog/the-day-you-stopped-breaking-y...
[dbt Labs employee here]
Check out dbt-expectations package[1]. It's a port of the Great Expectations checks to dbt as tests. The advantage of this is you don't need another tool for these pretty standard tests, and can be early incorporated into dbt workflows.
[1] https://github.com/calogica/dbt-expectations
In ETL Pipelines with Airflow: The Good, the Bad and the Ugly