swiple
soda-sql
| swiple | soda-sql | |
|---|---|---|
| 1 | 25 | |
| 78 | 50 | |
| - | - | |
| 0.0 | 8.2 | |
| 5 days ago | over 1 year ago | |
| Python | Python | |
| GNU General Public License v3.0 or later | Apache License 2.0 |
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swiple
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FastAPI + Poetry Docker Image, 3.7x size reduction
Just finished modifying a FastAPI + Poetry Docker image and reduced the image size by 3.7x. The Docker image originated from Jason Adam and thought other might find it valuable. All the code can be found here: https://github.com/Swiple/swiple/blob/main/backend/Dockerfile
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?
Flight-Test-Data-Analytics-Module-01 - Code to support Module 01 of the Daedalus Aerospace Flight Test Data Analytics course.
deequ - Deequ is a library built on top of Apache Spark for defining "unit tests for data", which measure data quality in large datasets.
data-diff - Compare tables within or across databases
pandera - A light-weight, flexible, and expressive statistical data testing library
cleanlab - The standard data-centric AI package for data quality and machine learning with messy, real-world data and labels.
sqlfluff - A modular SQL linter and auto-formatter with support for multiple dialects and templated code.
soda-core - :zap: Data quality testing for the modern data stack (SQL, Spark, and Pandas) https://www.soda.io
dbt-sessionization - Using DBT for Creating Session Abstractions on RudderStack - an open-source, warehouse-first customer data pipeline and Segment alternative.
panda_patrol
re_data - re_data - fix data issues before your users & CEO would discover them 😊
trino_data_mesh - Proof of concept on how to gain insights with Trino across different databases from a distributed data mesh
spark-fast-tests - Apache Spark testing helpers (dependency free & works with Scalatest, uTest, and MUnit)