cuallee
Possibly the fastest DataFrame-agnostic quality check library in town. (by canimus)
great_expectations
Always know what to expect from your data. (by great-expectations)
cuallee | great_expectations | |
---|---|---|
5 | 15 | |
107 | 9,479 | |
- | 1.0% | |
9.0 | 9.9 | |
6 days ago | 3 days ago | |
Python | Python | |
Apache License 2.0 | Apache License 2.0 |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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.
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.
cuallee
Posts with mentions or reviews of cuallee.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-11-30.
- Show HN: Snowflake Data Quality Checks in Python
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data-diff VS cuallee - a user suggested alternative
2 projects | 30 Nov 2022
Declarative data quality rules at scale
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deequ VS cuallee - a user suggested alternative
2 projects | 30 Nov 2022
Cuallee offers a faster and optimized version of pydeequ, on the Check API through the use of the new Observation API in pyspark. As well as support to Snowpark, Pandas, Polars and DuckDB dataframe abstractions.
- Show HN: Pyspark and Snowpark and Pandas data quality
- Show HN: Cuallee – pyspark data quality framework for v3.3.0
great_expectations
Posts with mentions or reviews of great_expectations.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-04-24.
-
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
-
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.