dbt-data-reliability
soda-sql
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dbt-data-reliability | soda-sql | |
---|---|---|
2 | 25 | |
338 | 50 | |
4.7% | - | |
9.7 | 8.2 | |
7 days ago | over 1 year ago | |
Python | Python | |
Apache License 2.0 | 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.
dbt-data-reliability
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How to store dbt run and test results in tables + code example
The entire implementation is available in our open source dbt package.
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Launch HN: Elementary (YC W22) β Open-source data observability
For any dbt users, their reliability package has the best and most comprehensive way to upload artifacts directly to the warehouse after a dbt invocation.
https://github.com/elementary-data/dbt-data-reliability
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! Itβs not requested that much π but please add an issue on GitHub . I would love to add at least experimental support.
What are some alternatives?
deequ - Deequ is a library built on top of Apache Spark for defining "unit tests for data", which measure data quality in large datasets.
elementary - The dbt-native data observability solution for data & analytics engineers. Monitor your data pipelines in minutes. Available as self-hosted or cloud service with premium features.
pandera - A light-weight, flexible, and expressive statistical data testing library
re_data - re_data - fix data issues before your users & CEO would discover them π
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.
sqllineage - SQL Lineage Analysis Tool powered by Python
versatile-data-kit - One framework to develop, deploy and operate data workflows with Python and SQL.
trino_data_mesh - Proof of concept on how to gain insights with Trino across different databases from a distributed data mesh