airflow-docker
soda-sql
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| airflow-docker | soda-sql | |
|---|---|---|
| 1 | 25 | |
| 21 | 50 | |
| - | - | |
| 7.0 | 8.2 | |
| 2 months ago | over 1 year ago | |
| Python | Python | |
| MIT License | Apache License 2.0 |
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.
airflow-docker
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Airflow Api tests
Clone the airflow-docker repo.
soda-sql
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Data Quality - Great Expectations for Data Engineers
I might be a bit biased, but that was my opinion before even I started contributing to Soda SQL.
- dbt vs R/Python for transformation
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SodaCL - preview of a new "data reliability as code" language
I'm one of the developers of the Open Source soda-sql data quality monitoring library, and over the past year we got some incredible feedback from our users, and based on that we started working on a new DSL for data reliability as code we are calling Soda CL.
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How do you test your pipelines?
You can also use soda-sql to do checks on your warehouses separately. Both Soda SQL and Soda Spark are OSS/Apache licensed.
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Being constantly shut down by more senior team members when I mention adding some QA in our work
As many have said, there might be business side of things to deliver. Somebody above promised delivery with tight deadlines. Trust me, I am not a fan, but this how the world works and it sucks. I would say in your free time, explore tools like greatexpectations.io https://greatexpectations.io/ or https://github.com/sodadata/soda-sql which are modern ways of testing in your learning curve
- Soda
- How heavily do you use Great Expectations?
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What are some exciting new tools/libraries in 2021?
soda-sql really cool library to automate data quality checks on SQL tables
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How do I incorporate testing after the fact?
Look at SodaSQL. It's more enterprise focused than Great Expectations and you can pipe results to a database for downstream actions and analysis.
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Data Testing Tools, Pytest vs Great Expectations vs Soda vs Deequ
Certainly! It’s not requested that much 😊 but please add an issue on GitHub . I would love to add at least experimental support.
What are some alternatives?
wsl-windows-toolbar-launcher - Adds linux GUI application menu to a windows toolbar
deequ - Deequ is a library built on top of Apache Spark for defining "unit tests for data", which measure data quality in large datasets.
superset - Apache Superset is a Data Visualization and Data Exploration Platform
pandera - A light-weight, flexible, and expressive statistical data testing library
nft-starter-kit - Timescale NFT Starter Kit
sqlfluff - A modular SQL linter and auto-formatter with support for multiple dialects and templated code.
airflow-api-tests - This is a collection of Pytest for the 2.0 Stable Rest Apis for Apache Airflow. I have another repo where you could setup airflow locally and play around with these. I am used to RestAssured, but trying out pytest here.
dbt-sessionization - Using DBT for Creating Session Abstractions on RudderStack - an open-source, warehouse-first customer data pipeline and Segment alternative.
portable-data-stack-dagster - A portable Datamart and Business Intelligence suite built with Docker, Dagster, dbt, DuckDB, PostgreSQL and Superset
re_data - re_data - fix data issues before your users & CEO would discover them 😊
cargo-crates - An easy way to build data extractors in Docker.
trino_data_mesh - Proof of concept on how to gain insights with Trino across different databases from a distributed data mesh