great_expectations
castled
| great_expectations | castled | |
|---|---|---|
| 15 | 12 | |
| 9,466 | 316 | |
| 0.9% | - | |
| 9.9 | 9.9 | |
| 5 days ago | about 2 years ago | |
| Python | Java | |
| Apache License 2.0 | GNU General Public License v3.0 or later |
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.
castled
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Data pipeline suggestions
Reverse ETL: Grouparoo, Castled
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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
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Launch HN: Castled Data (YC W22) – Open-Source Reverse ETL
Thanks. We also have a subscription based hosted solution hosted at https://castled.io
- Castled - an open source reverse ETL solution that helps you to periodically sync the data in your db/warehouse into sales, marketing, support or custom apps without any help from engineering teams
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Castled.io is the fastest Reverse ETL platform till date
Castled is 6x to 13x faster than other Reverse ETL platforms like Census and Hightouch! The project is open source.
- Castled is an open source reverse ETL solution that helps you to periodically sync the data in your warehouses and databases to sales, marketing, support or custom apps without any help from engineering teams
- Castled – Reverse ETL
What are some alternatives?
evidently - Evaluate and monitor ML models from validation to production. Join our Discord: https://discord.com/invite/xZjKRaNp8b
lightdash - Self-serve BI to 10x your data team ⚡️
kedro-great - The easiest way to integrate Kedro and Great Expectations
X-Road - Source code of the X-Road® data exchange layer software
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.
datahelix - The DataHelix generator allows you to quickly create data, based on a JSON profile that defines fields and the relationships between them, for the purpose of testing and validation
re_data - re_data - fix data issues before your users & CEO would discover them 😊
monosi - Open source data observability platform
streamlit - Streamlit — A faster way to build and share data apps.
dbt-core - dbt enables data analysts and engineers to transform their data using the same practices that software engineers use to build applications.
seldon-core - An MLOps framework to package, deploy, monitor and manage thousands of production machine learning models
projects - Sample projects using Ploomber.