Explore the comprehensive DataOps Stack, designed for modern collaboration, automation, and continuous delivery of data. This open-source stack includes top-rated tools and libraries to optimize your data pipelines, improve data quality, and accelerate data delivery. Discover the essential repositories and categories that make DataOps a game-changer for data-driven organizations.
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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airbytehq/
airbyte

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amundsen-io/
amundsen

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apache/
airflow

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apache/
iceberg

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awslabs/
deequ

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dagster-io/
dagster

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datahub-project/
datahub

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dbt-labs/
dbt-core

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delta-io/
delta

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great-expectations/
great_expectations

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hashicorp/
terraform

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iterative/
dvc

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kubernetes/
kubernetes

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meltano/
meltano

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prometheus/
prometheus

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singer-io/
singer

Use this link to track latest updates across the 19 repositories in DataOps Stack

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Staying up-to-date with latest features of the
DataOps Stack in 2026

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 DataOps Stack empowers organizations to accelerate data delivery with agility, collaboration, automation, and continuous delivery for data. By leveraging a robust ecosystem of open-source tools, teams can streamline data workflows, enhance data quality, foster collaboration between data engineers, analysts, and operations, and automate the entire data lifecycle from ingestion to deployment. The DataOps Stack is essential for modern data-driven organizations seeking to achieve scalable, reliable, and reproducible data operations.

Here's a breakdown of the DataOps Stack into different categories

Orchestration & Workflow Management

Orchestration & workflow management tools are at the core of DataOps, enabling the automation, scheduling, and monitoring of complex data pipelines. These tools ensure the reliable execution of tasks and facilitate collaboration among teams.

Apache Airflow

apache/airflow
A platform to programmatically author, schedule, and monitor workflows.
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Prefect

PrefectHQ/prefect
A modern workflow orchestration framework for data-intensive pipelines.
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Dagster

dagster-io/dagster
An orchestration platform for the development, production, and observation of data assets.
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Data Version Control & Lineage

Data versioning and lineage are critical for reproducibility, governance, and traceability of data flows in DataOps. These tools help track data changes and movements across the pipeline.

DVC

iterative/dvc
Data Version Control - Git for data & models, making ML reproducible and shareable.
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OpenLineage

OpenLineage/OpenLineage
An open standard for metadata and lineage collection designed for the modern data ecosystem.
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Data Quality & Testing

Data quality and testing tools help ensure data reliability, correctness, and consistency across the pipeline, fostering trust in data products.

Great Expectations

great-expectations/great_expectations
Always know what to expect from your data. Flexible, open-source, and extensible data quality validation.
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Deequ

awslabs/deequ
Deequ is a library built on top of Apache Spark for defining 'unit tests' for data, which are used to measure data quality.
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Data Transformation & Modeling

Data transformation and modeling tools automate and document the transformation of raw data into meaningful insights, forming the backbone of analytics in DataOps.

dbt

dbt-labs/dbt-core
dbt enables analytics engineers to transform data in their warehouse more effectively.
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Meltano

meltano/meltano
Open-source DataOps platform for ELT pipelines that brings together extraction, loading, and transformation.
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Data Ingestion & Integration

Data ingestion and integration tools facilitate the collection and synchronization of data from disparate sources into centralized data stores.

Airbyte

airbytehq/airbyte
Open-source data integration platform that syncs data from APIs, databases, and files to data warehouses, lakes, and other destinations.
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Singer

singer-io/singer
Open standard for writing scripts that move data, supporting ETL and ELT workflows.
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new features in Singer?

Collaboration & Documentation

Collaboration and documentation tools support team communication, data cataloging, and knowledge sharing, which are key for DataOps success.

Amundsen

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

datahub-project/datahub
A metadata platform for the modern data stack, helping teams discover, manage, and govern data.
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new features in DataHub?

Monitoring & Observability

Monitoring and observability tools provide real-time insights into the health and performance of data pipelines, supporting proactive issue resolution.

Marquez

MarquezProject/marquez
An open-source metadata service for the collection, aggregation, and visualization of a data ecosystem’s metadata.
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new features in Marquez?

Prometheus

prometheus/prometheus
Open-source monitoring and alerting toolkit, ideal for monitoring data infrastructure components.
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Infrastructure as Code (IaC) & Deployment

IaC and deployment tools automate infrastructure provisioning and deployment, ensuring reproducible, scalable, and consistent environments for DataOps.

Terraform

hashicorp/terraform
Open-source infrastructure as code software tool for building, changing, and versioning infrastructure safely and efficiently.
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Kubernetes

kubernetes/kubernetes
Open-source system for automating deployment, scaling, and management of containerized applications.
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Data Store & Lakehouse

Data stores and lakehouse technologies provide scalable, reliable, and efficient storage solutions for structured and unstructured data.

Delta Lake

delta-io/delta
An open-source storage layer that brings ACID transactions to Apache Spark and big data workloads.
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Apache Iceberg

apache/iceberg
A high-performance format for huge analytic tables that brings SQL tables to big data.
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Explore the latest releases and features of the DataOps Stack by visiting the repository links above. Unlock the full potential of your data operations by integrating these cutting-edge open-source tools—click the URLs to get started and stay at the forefront of DataOps innovation!