dbt-spotify-analytics
soda-sql
| dbt-spotify-analytics | soda-sql | |
|---|---|---|
| 1 | 25 | |
| 120 | 50 | |
| - | - | |
| 0.0 | 8.2 | |
| almost 2 years 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.
dbt-spotify-analytics
soda-sql
-
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
-
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.
-
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.
-
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?
-
What are some exciting new tools/libraries in 2021?
soda-sql really cool library to automate data quality checks on SQL tables
-
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.
-
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?
frappe - Low code web framework for real world applications, in Python and Javascript
deequ - Deequ is a library built on top of Apache Spark for defining "unit tests for data", which measure data quality in large datasets.
wal-e - Continuous Archiving for Postgres
pandera - A light-weight, flexible, and expressive statistical data testing library
Skytrax-Data-Warehouse - A full data warehouse infrastructure with ETL pipelines running inside docker on Apache Airflow for data orchestration, AWS Redshift for cloud data warehouse and Metabase to serve the needs of data visualizations such as analytical dashboards.
sqlfluff - A modular SQL linter and auto-formatter with support for multiple dialects and templated code.
airflow-dbt - Apache Airflow integration for dbt
dbt-sessionization - Using DBT for Creating Session Abstractions on RudderStack - an open-source, warehouse-first customer data pipeline and Segment alternative.
trino_data_mesh - Proof of concept on how to gain insights with Trino across different databases from a distributed data mesh
re_data - re_data - fix data issues before your users & CEO would discover them 😊
spotify-data-reader