pointblank
piperider
| pointblank | piperider | |
|---|---|---|
| 3 | 6 | |
| 826 | 467 | |
| 1.3% | -0.2% | |
| 9.4 | 9.5 | |
| about 1 month ago | about 2 months 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
piperider
- Show HN: PipeRider – open-source Data Impact Analysis for dbt changes
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Open source data observability tools with UI?
If you post a GitHub issue to request these connectors is might help persuade the product team to add these sooner than later.
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Data profiling as part of a data reliability strategy?
PS. I'm a bit biased -> I'm working for PipeRider; we're building an open-source data reliability toolkit with profiling at the core: https://github.com/InfuseAI/piperider
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Show HN: PipeRider, data reliability automated tool
I was rush to Show HN, and now I want to tell a bit more.
PipeRider, it’s our take on a data reliability and quality tool for data pipelines. It’s based on data profiling and assertions that test against the data profile.
It’s open-source and ready to use on Github here: https://github.com/infuseai/piperider
Here is a quick start to get you up and running easily:
What are some alternatives?
pandera - A light-weight, flexible, and expressive statistical data testing library
soda-sql - Data profiling, testing, and monitoring for SQL accessible data.
allure-environment-writer - Java library which allows to write environment.xml file into allure-results directory.
great_expectations - Always know what to expect from your data.
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.
pandas-profiling - Create HTML profiling reports from pandas DataFrame objects [Moved to: https://github.com/ydataai/pandas-profiling]
soda-core - :zap: Data quality testing for the modern data stack (SQL, Spark, and Pandas) https://www.soda.io
ydata-profiling - 1 Line of code data quality profiling & exploratory data analysis for Pandas and Spark DataFrames.
DataProfiler - What's in your data? Extract schema, statistics and entities from datasets
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.
dbt-oracle - dbt (data build tool) adapter for Oracle Autonomous Database