dbt-unit-testing
fugue
dbt-unit-testing | fugue | |
---|---|---|
7 | 11 | |
404 | 1,880 | |
1.5% | 1.4% | |
7.7 | 6.4 | |
16 days ago | 6 days ago | |
Shell | Python | |
MIT License | Apache License 2.0 |
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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.
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
fugue
- FLaNK Stack Weekly 22 January 2024
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Daft: A High-Performance Distributed Dataframe Library for Multimodal Data
Please integrate it with Fugue.
https://github.com/fugue-project/fugue
- Fugue: A unified interface for distributed computing
- [Discussion] Open Source beats Google's AutoML for Time series
- Ask HN: How do you test SQL?
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Replacing Pandas with Polars. A Practical Guide
Fugue is an interesting library in this space , though I haven’t tried it
https://github.com/fugue-project/fugue
A unified interface for distributed computing. Fugue executes SQL, Python, and Pandas code on Spark, Dask and Ray without any rewrites.
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The hand-picked selection of the best Python libraries and tools of 2022
fugue — distributed computing done easy
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[P] Open data transformations in Python, no SQL required
This looks similar to fugue, am I right? How do they compare?
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What the Duck?!
I am looking forward to how Substrait could help removing this friction. It aims to provide a standardised intermediate query language (lower level than SQL) to connect frontend user interfaces like SQL or data frame libraries with backend analytical computing engines. It is linked to the Arrow ecosystem. Something like Ibis or Fugue could become the front and DuckDB the backend engine.
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Pyspark now provides a native Pandas API
There's dask-sql, but I think it is being abandoned for fugue-project. I'm actually excited for this project as it is trying to provide a backend agnostic solution, which would seem like a difficult, lofty goal. I wish them luck.
What are some alternatives?
sqlglot - Python SQL Parser and Transpiler
modin - Modin: Scale your Pandas workflows by changing a single line of code
data-diff - Compare tables within or across databases
data-science-ipython-notebooks - Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.
dbt-expectations - Port(ish) of Great Expectations to dbt test macros
Optimus - :truck: Agile Data Preparation Workflows made easy with Pandas, Dask, cuDF, Dask-cuDF, Vaex and PySpark
sqlx - 🧰 The Rust SQL Toolkit. An async, pure Rust SQL crate featuring compile-time checked queries without a DSL. Supports PostgreSQL, MySQL, and SQLite.
mlToolKits - learningOrchestra is a distributed Machine Learning integration tool that facilitates and streamlines iterative processes in a Data Science project.
SS-Unit - A 100% T-SQL based unit testing framework for SQL Server
ploomber - The fastest ⚡️ way to build data pipelines. Develop iteratively, deploy anywhere. ☁️
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
xarray - N-D labeled arrays and datasets in Python