Explore the comprehensive Data Quality Stack, including Great Expectations, Deequ, and Apache Griffin, to optimize your data integrity and governance. Learn how this stack facilitates robust data validation, profiling, and monitoring with a vibrant ecosystem of open source libraries. Enhance your data pipelines and ensure high-quality analytics with the best-in-class data quality tools.
feature.delivery is a free, web-based platform that enables developers to monitor and consolidate software releases from multiple GitHub repositories into a single, streamlined chronological view. By centralizing release information across various tools, libraries, and services, feature.delivery simplifies the process of staying informed about the latest updates in a development stack. Stay ahead of the curve with feature.delivery, the free online tool designed to help developers effortlessly track and consolidate the latest releases from multiple GitHub repositories in one clean, chronological view. Whether you're managing a complex development stack or simply want to stay up to date with your favorite open-source projects, feature.delivery streamlines release tracking so you never miss an important update. By keeping up with the latest changes, developers can quickly adopt new features, enhance performance, and maintain a competitive edge in today's fast-moving tech landscape. Say goodbye to manual tracking and hello to smarter, faster development with feature.delivery.

https://feature.delivery

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MarquezProject/
marquez

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OpenLineage/
OpenLineage

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PrefectHQ/
prefect

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amundsen-io/
amundsen

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apache/
airflow

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apache/
griffin

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awslabs/
deequ

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calogica/
dbt-expectations

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dagster-io/
dagster

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datahub-project/
datahub

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great-expectations/
awesome-expectations

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great-expectations/
great_expectations

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great-expectations/
great_expectations-dbt

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pandas-profiling/
pandas-profiling

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sodadata/
soda-core

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ydataai/
ydata-profiling

Use this link to track latest updates across the 16 repositories in Data Quality Stack

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Staying up-to-date with latest features of the
Data Quality Stack in 2025

How does it work?

feature.delivery is a free, web-based platform that helps developers track the latest releases from multiple GitHub repositories — all in one streamlined, chronological view. By centralizing release information across tools, libraries, and frameworks, feature.delivery makes it easier than ever to stay on top of the updates throughout your development stack.

Checkout this 1 minute intro video to see it in action

The Data Quality Stack, featuring Great Expectations, Deequ, and Apache Griffin, is a powerful suite of open source tools designed to ensure data integrity, reliability, and trustworthiness across data pipelines. Leveraging this stack helps organizations automate data validation, profiling, and cleansing, thereby reducing errors, improving compliance, and boosting confidence in data-driven decision-making. With a rich ecosystem and active community, the Data Quality Stack stands out as a leading solution for maintaining high standards in data engineering workflows.

Here's a breakdown of the Data Quality Stack into different categories

Core Data Quality Libraries

These libraries form the backbone of the Data Quality Stack, providing essential frameworks for data validation, profiling, and governance. They are widely adopted for their scalability, flexibility, and integration capabilities across data environments.

Great Expectations

great-expectations/great_expectations
A leading open-source library for data validation, documentation, and profiling, supporting a wide range of data sources.
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Deequ

awslabs/deequ
A scalable library built on Apache Spark for defining and running data quality checks on large datasets.
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Apache Griffin

apache/griffin
An open source data quality solution for big data, offering both batch and streaming data quality measurement.
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Profiling and Monitoring Tools

These tools provide advanced capabilities for data profiling, monitoring, and anomaly detection, essential for maintaining ongoing data quality in dynamic environments.

Soda Core

sodadata/soda-core
A modern open source data monitoring tool for detecting, analyzing, and resolving data quality issues.
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OpenLineage

OpenLineage/OpenLineage
A standard for metadata and data lineage collection, enabling data quality tracking across pipelines.
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Marquez

MarquezProject/marquez
An open source metadata service for the collection, aggregation, and visualization of data lineage.
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Data Profiling and Exploration

Libraries focused on automated data profiling and exploration to understand data distributions, outliers, and data schema before implementing data quality checks.

pandas-profiling

pandas-profiling/pandas-profiling
Generates extensive profiling reports from pandas DataFrames, aiding in initial data quality assessment.
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ydata-profiling

ydataai/ydata-profiling
Successor to pandas-profiling, providing enhanced data profiling and report generation for dataframes.
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Data Quality Orchestration & Workflow

These tools support the orchestration and automation of data quality checks as part of broader ETL and data pipeline workflows.

Dagster

dagster-io/dagster
An orchestration platform for data assets, making it easy to include data quality checks in pipeline workflows.
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Airflow

apache/airflow
A platform to programmatically author, schedule, and monitor workflows, often used to schedule data quality checks.
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Prefect

PrefectHQ/prefect
A workflow management system that makes it easy to build, run, and monitor data quality tasks.
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Data Quality for Data Lakes and Warehouses

Specialized libraries and plugins for implementing data quality checks within popular data lake and data warehouse environments.

great_expectations-expectations

great-expectations/awesome-expectations
A repository of community-contributed Great Expectations expectations for various data sources.
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dbt-expectations

calogica/dbt-expectations
A dbt package for data testing using Great Expectations-style checks within your dbt workflows.
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great-expectations-dbt

great-expectations/great_expectations-dbt
Community plugin to integrate Great Expectations with dbt for seamless data quality checks.
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Data Governance and Metadata

Tools that enhance data governance by providing metadata management, data cataloging, and lineage for better quality control.

Amundsen

amundsen-io/amundsen
A data discovery and metadata platform for improving the productivity of data analysts, data scientists, and engineers.
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DataHub

datahub-project/datahub
An open-source metadata platform for the modern data stack, enabling rich metadata management and lineage.
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Discover the full potential of the Data Quality Stack by exploring each repository’s release page. Click on the URLs to see the latest features, updates, and integrations that can enhance your data quality initiatives.