great_expectations
soda-core
Our great sponsors
| great_expectations | soda-core | |
|---|---|---|
| 15 | 5 | |
| 9,466 | 1,751 | |
| 1.7% | 3.8% | |
| 9.9 | 9.0 | |
| about 8 hours ago | 3 days ago | |
| Python | Python | |
| Apache License 2.0 | 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.
great_expectations
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Data Quality at Scale with Great Expectations, Spark, and Airflow on EMR
Great Expectations (GE) is an open-source data validation tool that helps ensure data quality.
- Looking for Unit Testing framework in Database Migration Process
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Soda Core (OSS) is now GA! So, why should you add checks to your data pipelines?
GE is arguably the most well known OSS alternative to Soda Core. The third option is deequ, originally developed and released in OSS by AWS. Our community has told us that Soda Core is different because it’s easy to get going and embed into data pipelines. And it also allows some of the check authoring work to be moved to other members of the data team. I'm sure there are also scenarios where Soda Core is not the best option. For example, when you only use Pandas dataframes or develop in Scala.
- Greatexpectations - Always know what to expect from your data.
- Greatexpectations – Always know what to expect from your data
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Package for drift detection
great_expectations: https://github.com/great-expectations/great_expectations
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[D] Do you use data engineering pipelines for real life projects?
For example I just found "Great Expectations" and "Kedro", "Flyte" and I was wondering at which point in time and project complexity should we choose one of these tools instead of the ancient cave man way?
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Data pipeline suggestions
Testing: GreatExpectations
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Where can I find free data engineering ( big data) projects online?
Ingestion / ETL: Airbyte, Singer, Jitsu Transformation: dbt Orchestration: Airflow, Dagster Testing: GreatExpectations Observability: Monosi Reverse ETL: Grouparoo, Castled Visualization: Lightdash, Superset
- [P] Deepchecks: an open-source tool for high standards validations for ML models and data.
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?
evidently - Evaluate and monitor ML models from validation to production. Join our Discord: https://discord.com/invite/xZjKRaNp8b
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.
kedro-great - The easiest way to integrate Kedro and Great Expectations
dictum - Describe business metrics with YAML, query and visualize in Jupyter with zero SQL
deepchecks - Deepchecks: Tests for Continuous Validation of ML Models & Data. Deepchecks is a holistic open-source solution for all of your AI & ML validation needs, enabling to thoroughly test your data and models from research to production.
cuallee - Possibly the fastest DataFrame-agnostic quality check library in town.
re_data - re_data - fix data issues before your users & CEO would discover them 😊
data-diff - Compare tables within or across databases
streamlit - Streamlit — A faster way to build and share data apps.
dbt-snowflake-monitoring - A dbt package from SELECT to help you monitor Snowflake performance and costs
seldon-core - An MLOps framework to package, deploy, monitor and manage thousands of production machine learning models
pointblank - Data quality assessment and metadata reporting for data frames and database tables