pg_temp
dbt-unit-testing
| pg_temp | dbt-unit-testing | |
|---|---|---|
| 1 | 7 | |
| 13 | 409 | |
| - | 1.2% | |
| 0.0 | 7.7 | |
| 2 months ago | about 2 months ago | |
| Python | Shell | |
| MIT License | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
pg_temp
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Ask HN: How do you test SQL?
Here's a nifty python module for testing against postgres that a friend wrote. It creates a temporary database on the fly for testing and tears it down at the end. You can then populate the database and run all kinds of integration tests against it.
https://github.com/ugtar/pg_temp
dbt-unit-testing
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The SQL Unit Testing Landscape: 2023
If you use dbt for transformations Dbt Unit Testing (https://github.com/EqualExperts/dbt-unit-testing) is getting some attention (https://www.thoughtworks.com/radar/languages-and-frameworks?blipid=202304042)
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Data-eng related highlights from the latest Thoughtworks Tech Radar
dbt-unit-testing
- I'm not getting it...what's the point of DBT?
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Ask HN: How do you test SQL?
We use this and take an example-based tests approach for any non-trivial tables: https://github.com/EqualExperts/dbt-unit-testing
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SQL should be your default choice for data engineering pipelines
> How do you test some SQL logic in isolation?
I do this using sql
1. Extracting an 'ephemeral model' to different model file
2. Mock out this model in upstream model in unit tests https://github.com/EqualExperts/dbt-unit-testing
3. Write unit tests for this model.
This is not different than regular software development in a language like java.
I would argue its even better better because unit tests are always in tabular format and pretty easy to understand. Java unit tests on other hand are never read by devs in practice.
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Unit testing with dbt
I haven't done it yet but there are some popular blogs as well as a DBT package someone created.
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Modern Data Modeling: Start with the End?
> I really don’t understand the communities obsession with unwieldy tools like DBT.
It lets me write test first sql transforms. I never thought TDD sql would be possible. My sql is so much more readable with common logic extracted into ephmeral models. I practice same method to write clear code to write sql, eg: too many mocks = refactor into separate model ( class) .
I think DBT made this possible with refs that can be swapped out with mocks. This is the awesome library I am using https://github.com/EqualExperts/dbt-unit-testing
What are some alternatives?
spark-style-guide - Spark style guide
sqlglot - Python SQL Parser and Transpiler
sqlfluff - A modular SQL linter and auto-formatter with support for multiple dialects and templated code.
data-diff - Compare tables within or across databases
SS-Unit - A 100% T-SQL based unit testing framework for SQL Server
dbt-expectations - Port(ish) of Great Expectations to dbt test macros
datajudge - Assessing whether data from database complies with reference information.
sqlx - 🧰 The Rust SQL Toolkit. An async, pure Rust SQL crate featuring compile-time checked queries without a DSL. Supports PostgreSQL, MySQL, and SQLite.
fugue - A unified interface for distributed computing. Fugue executes SQL, Python, Pandas, and Polars code on Spark, Dask and Ray without any rewrites.
hash-db - Experimental distributed pseudomultimodel keyvalue database (it uses python dictionaries) imitating dynamodb querying with join only SQL support, distributed joins and simple Cypher graph support and document storage