Topics / #data-quality

Data-quality projects on GitHub

Profiling, validating and monitoring the quality of datasets.

As of , the most-starred Data-quality projects on GitHub are Made-With-ML (49.8K stars), data-engineer-handbook (44.5K stars) and applied-ml (30.6K stars). GitHub.Locker tracks 12 Data-quality repositories with 189.9K stars combined. The fastest-growing on October 16 was OpenMetadata, with +14 stars in a day.

12Projects
189.9KStars in total
+51Stars in a day
Browse in the catalog →github.com/topics/data-quality ↗

Hot right now

Most stars gained in a day · Oct 16

+14

OpenMetadata

open-metadata/OpenMetadata · ★ 15.5K

OpenMetadata is an open platform that unifies technical metadata, lineage, data quality, ownership and business glossaries into one knowledge graph so both humans and AI agents can discover, trust and reuse context about enterprise data.

+10

Made-With-ML

GokuMohandas/Made-With-ML · ★ 49.8K

Made-With-ML is a free hands-on course that teaches how to design, develop, deploy, and iterate on production-grade machine learning applications, combining ML with software engineering best practices.

+9

data-engineer-handbook

DataExpert-io/data-engineer-handbook · ★ 44.5K

A curated handbook of links and resources for learning data engineering: roadmaps, bootcamp materials, books, communities, company tooling lists, blogs, whitepapers, and a directory of data engineering content creators.

+8

applied-ml

eugeneyan/applied-ml · ★ 30.6K

applied-ml is a curated collection of papers, articles, and tech blogs where companies like Airbnb, Uber, Netflix, Google, and DoorDash share how they run data science and machine learning in production.

The all-time top

12 projects · by stars

  1. #1 · GokuMohandas/Made-With-ML

    Made-With-ML

    Made-With-ML is a free hands-on course that teaches how to design, develop, deploy, and iterate on production-grade machine learning applications, combining ML with software engineering best practices.

    49.8K
  2. #2 · DataExpert-io/data-engineer-handbook

    data-engineer-handbook

    A curated handbook of links and resources for learning data engineering: roadmaps, bootcamp materials, books, communities, company tooling lists, blogs, whitepapers, and a directory of data engineering content creators.

    44.5K
  3. #3 · eugeneyan/applied-ml

    applied-ml

    applied-ml is a curated collection of papers, articles, and tech blogs where companies like Airbnb, Uber, Netflix, Google, and DoorDash share how they run data science and machine learning in production.

    30.6K
  1. 4

    OpenMetadata

    15.5Kopen-metadata/OpenMetadata · TypeScript

    OpenMetadata is an open platform that unifies technical metadata, lineage, data quality, ownership and business glossaries into one knowledge graph so both humans and AI agents can discover, trust and reuse context about enterprise data.

  2. 5

    fg-data-profiling

    13.7KData-Centric-AI-Community/fg-data-profiling · Python

    fg-data-profiling (formerly ydata-profiling / pandas-profiling) is a Python package that generates a one-line exploratory data analysis (EDA) report for pandas or Spark DataFrames, similar to df.describe() but far more detailed.

  3. 6

    great_expectations

    11.9Kfivetran/great_expectations · Python

    GX Core (Great Expectations) is a Python framework for testing, validating, and documenting the quality of your data. Its core concept is the 'Expectation' — an expressive unit test for your data, built from the collective experience of thousands of data quality deployments.

  4. 7

    cleanlab

    11.7Kcleanlab/cleanlab · Python

    Cleanlab is an open-source Python library that automatically finds issues in ML datasets — label errors, outliers, duplicates — using your existing models, so that fixing the data trains better models.

  5. 8

    fiftyone

    11.2Kvoxel51/fiftyone · TypeScript

    FiftyOne is an open-source tool for building high-quality datasets and computer vision models: you use it to visualize and label your visual data, evaluate models, and find and fix data quality issues.

  6. 9

    huashu-excel

    439alchaincyf/huashu-excel · Python

    This is a Skill for AI coding agents that handles the entire Excel/data-analysis workflow: it profiles a messy spreadsheet, cleans it, aligns with the user on what to actually analyze, runs the analysis, reconciles the numbers against the sheet's own totals, and only then delivers a report.

  7. 10

    hflow

    298Hebbian-Robotics/hflow · Python

    HFlow is an open source SDK from Hebbian Robotics (YC S26) for building scalable multimodal data pipelines for robotics and physical AI, letting teams verify the quality of the data they use to train AI models.

  8. 11

    full-stack-data-engineering

    145schumannx/full-stack-data-engineering · Python

    A full reference implementation of a streaming data warehouse on AWS: playback events flow from Kafka into a five-zone S3 Iceberg lake, get modeled with Kimball dimensional design, and are served through Athena SQL and HTML reports, all orchestrated by self-hosted Apache Airflow 3.

  9. 12

    workbooklens

    142chenweixin123/workbooklens · Python

    WorkbookLens is a local-first tool that lints, regression-tests, semantic-diffs, and conservatively repairs Excel workbooks, running entirely on your machine without Excel, LibreOffice, an AI key, or a cloud service.

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