fastapi-dramatiq-data-ingestion
great_expectations
| fastapi-dramatiq-data-ingestion | great_expectations | |
|---|---|---|
| 1 | 15 | |
| 39 | 9,479 | |
| - | 1.0% | |
| 3.8 | 9.9 | |
| over 2 years ago | 3 days ago | |
| Python | Python | |
| MIT License | 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.
fastapi-dramatiq-data-ingestion
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Create and deploy a reliable data ingestion service with FastAPI, SQLModel and Dramatiq
Here is the GitHub repository with the source code of the app: https://github.com/frankie567/fastapi-dramatiq-data-ingestion
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.
What are some alternatives?
Prefect - The easiest way to build, run, and monitor data pipelines at scale.
evidently - Evaluate and monitor ML models from validation to production. Join our Discord: https://discord.com/invite/xZjKRaNp8b
Apache Superset - Apache Superset is a Data Visualization and Data Exploration Platform [Moved to: https://github.com/apache/superset]
kedro-great - The easiest way to integrate Kedro and Great Expectations
pydantic-factories - Simple and powerful mock data generation using pydantic or dataclasses
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.
ploomber - The fastest ⚡️ way to build data pipelines. Develop iteratively, deploy anywhere. ☁️
re_data - re_data - fix data issues before your users & CEO would discover them 😊
cookiecutter-django - Cookiecutter Django + PostGres + Docker + DramatiQ
streamlit - Streamlit — A faster way to build and share data apps.
fastapi-starter-project - FastAPI Starter Project with SQLModel and Alembic
seldon-core - An MLOps framework to package, deploy, monitor and manage thousands of production machine learning models