Skytrax-Data-Warehouse
soda-sql
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| Skytrax-Data-Warehouse | soda-sql | |
|---|---|---|
| 1 | 25 | |
| 131 | 50 | |
| - | - | |
| 0.0 | 8.2 | |
| about 4 years ago | over 1 year ago | |
| Python | Python | |
| MIT License | Apache License 2.0 |
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Skytrax-Data-Warehouse
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Open source contributions for a Data Engineer?
Always open to accept contributions to my project (Skytrax Data Warehouse). If you are into data stuff support my work at youtube as well (One Developer Pirate), I mostly make data-oriented videos. These days I'm making a SQL course from a data analysis perspective that is expected to release in next week.
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?
dbd - dbd is a database prototyping tool that enables data analysts and engineers to quickly load and transform data in SQL databases.
deequ - Deequ is a library built on top of Apache Spark for defining "unit tests for data", which measure data quality in large datasets.
sqlfluff - A modular SQL linter and auto-formatter with support for multiple dialects and templated code.
pandera - A light-weight, flexible, and expressive statistical data testing library
jaydebeapi - JayDeBeApi module allows you to connect from Python code to databases using Java JDBC. It provides a Python DB-API v2.0 to that database.
dbt-spotify-analytics - Containerized end-to-end analytics of Spotify data using Python, dbt, Postgres, and Metabase
dbt-sessionization - Using DBT for Creating Session Abstractions on RudderStack - an open-source, warehouse-first customer data pipeline and Segment alternative.
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.
re_data - re_data - fix data issues before your users & CEO would discover them 😊
dagster - An orchestration platform for the development, production, and observation of data assets.
trino_data_mesh - Proof of concept on how to gain insights with Trino across different databases from a distributed data mesh