gretel-airflow-pipelines
soda-sql
| gretel-airflow-pipelines | soda-sql | |
|---|---|---|
| 1 | 25 | |
| 6 | 50 | |
| - | - | |
| 0.0 | 8.2 | |
| over 2 years ago | over 1 year ago | |
| Python | Python | |
| - | Apache License 2.0 |
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gretel-airflow-pipelines
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Running Gretel on Apache Airflow - privacy engineering synthetics
Here's a link to the Github repo from the blog- https://github.com/gretelai/gretel-airflow-pipelines
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?
airflow-testing-ci-workflow - (project & tutorial) dag pipeline tests + ci/cd setup
deequ - Deequ is a library built on top of Apache Spark for defining "unit tests for data", which measure data quality in large datasets.
jina - ☁️ Build multimodal AI applications with cloud-native stack
pandera - A light-weight, flexible, and expressive statistical data testing library
Airflow - Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
sqlfluff - A modular SQL linter and auto-formatter with support for multiple dialects and templated code.
elyra - Elyra extends JupyterLab with an AI centric approach.
dbt-sessionization - Using DBT for Creating Session Abstractions on RudderStack - an open-source, warehouse-first customer data pipeline and Segment alternative.
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)
Prefect - The easiest way to build, run, and monitor data pipelines at scale.