pointblank
soda-core
| pointblank | soda-core | |
|---|---|---|
| 3 | 5 | |
| 826 | 1,765 | |
| 1.3% | 1.5% | |
| 9.4 | 8.9 | |
| about 1 month ago | 2 days ago | |
| R | Python | |
| GNU General Public License v3.0 or later | 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.
pointblank
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R: Introduction to Data Science
(1) You might want to check out https://github.com/t-kalinowski/Rapp by my colleague Tomasz
(2) I think part of that is in scope for strict (https://github.com/hadley/strict). You might also be well served by adopting some more data validation tooling, e.g. pointblank (https://rstudio.github.io/pointblank/).
- Custom Formatting in pointblank
- Pointblank: R package for data validation
soda-core
- Looking for Unit Testing framework in Database Migration Process
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Data profiling tools / approaches?
Tools like Soda Core could be really helpful for this. For example, it allows you to set up a change over time threshold which could take the form of: change avg last 3 for missing_count(column_name) < 20%
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Data QC? Great Expectations?
You can give https://github.com/sodadata/soda-core - open source and (in my opinion) easy to get a lot of value with minimum effort.
- Show HN: Soda Core is now GA – Test data like you would test your code
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Soda Core (OSS) is now GA! So, why should you add checks to your data pipelines?
Give Soda Core a try! It's really easy. If you only have 2 minutes, check out our docs or interactive demo (pretty cool no?). If you have a bit more time, install it and give it a spin! Want to look at it later? Star on Github. Got stuck? As in our Slack community.
What are some alternatives?
pandera - A light-weight, flexible, and expressive statistical data testing library
great_expectations - Always know what to expect from your data.
allure-environment-writer - Java library which allows to write environment.xml file into allure-results directory.
dbt-data-reliability - dbt package that is part of 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.
allure-docker-service - This docker container allows you to see up to date reports simply mounting your "allure-results" directory in the container (for a Single Project) or your "projects" directory (for Multiple Projects). Every time appears new results (generated for your tests), Allure Docker Service will detect those changes and it will generate a new report automatically (optional: send results / generate report through API), what you will see refreshing your browser.
dictum - Describe business metrics with YAML, query and visualize in Jupyter with zero SQL
piperider - Code review for data in dbt
cuallee - Possibly the fastest DataFrame-agnostic quality check library in town.
DataProfiler - What's in your data? Extract schema, statistics and entities from datasets
data-diff - Compare tables within or across databases
dbt-snowflake-monitoring - A dbt package from SELECT to help you monitor Snowflake performance and costs
ydata-profiling - 1 Line of code data quality profiling & exploratory data analysis for Pandas and Spark DataFrames.