dbt-documentor
dbt-data-reliability
| dbt-documentor | dbt-data-reliability | |
|---|---|---|
| 1 | 2 | |
| 19 | 434 | |
| - | 5.3% | |
| 3.5 | 8.8 | |
| about 2 years ago | 13 days ago | |
| F# | 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-documentor
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) – 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
What are some alternatives?
optimus - Optimus is an easy-to-use, reliable, and performant workflow orchestrator for data transformation, data modeling, pipelines, and data quality management.
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.
F# Formatting - F# tools for generating documentation (Markdown processor and F# code formatter)
soda-core - :zap: Data quality testing for the modern data stack (SQL, Spark, and Pandas) https://www.soda.io
F# Data - F# Data: Library for Data Access
deequ - Deequ is a library built on top of Apache Spark for defining "unit tests for data", which measure data quality in large datasets.