Explore the Data Lakehouse Stack, featuring Databricks, Apache Iceberg, and Delta Lake, for next-generation analytics. This stack delivers reliability, scalability, open standards, and seamless integration with the modern data ecosystem. Learn about the core libraries, connectors, data governance tools, and various open source repositories that fuel innovation in the lakehouse architecture. Stay ahead with robust ACID transaction support, schema evolution, and compatibility with Apache Spark, Presto, Trino, and more.
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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acryldata/
datahub

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amundsen-io/
amundsen

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apache/
airflow

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atlas

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apache/
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apache/
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apache/
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dagster-io/
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delta-io/
delta

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delta-io/
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prestodb/
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treeverse/
lakeFS

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trinodb/
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trinodb/
trino

Use this link to track latest updates across the 18 repositories in Data Lakehouse Stack

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how to keep up with the latest features in Data Lakehouse Stack?
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how to track latest features in Data Lakehouse Stack?

Staying up-to-date with latest features of the
Data Lakehouse 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 Data Lakehouse Stack (Databricks, Apache Iceberg, Delta Lake) combines the scalability and low-cost storage of data lakes with the data management and reliability features of data warehouses. This stack enables unified analytics, real-time streaming, and seamless machine learning workflows, all while maintaining open standards and interoperability. The stack provides ACID transactions, schema enforcement, and compatibility with popular data processing engines, making it the go-to architecture for modern data engineering and analytics.

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

Core Table Formats

Core table formats ensure the foundational storage structure for the lakehouse, supporting ACID transactions, schema evolution, and time travel. These libraries are essential for reliable and performant data lake operations.

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, enabling reliable data lake operations at scale.
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Apache Hudi

apache/hudi
A transactional data lake framework that greatly simplifies incremental data processing and data pipeline development.
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Data Processing Engines

Data processing engines provide the compute layer for executing complex queries, ETL pipelines, and real-time analytics on lakehouse data.

Apache Spark

apache/spark
Unified analytics engine for large-scale data processing, with built-in modules for SQL, streaming, and machine learning.
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Trino

trinodb/trino
Distributed SQL query engine for big data, optimized for low-latency analytical queries across multiple data sources.
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Presto

prestodb/presto
Distributed SQL query engine for running interactive analytic queries against data sources of all sizes.
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Connectors and Integrations

Connectors bridge the core table formats with various data processing engines, cloud platforms, and BI tools, ensuring interoperability and flexible analytics.

delta-rs

delta-io/delta-rs
A Rust implementation of the Delta Lake protocol, providing bindings for Python, JVM, and other languages.
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iceberg-spark

apache/iceberg/tree/master/spark
Apache Iceberg's Spark integration module for seamless data format compatibility.
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trino-iceberg

trinodb/trino/tree/master/plugin/trino-iceberg
Trino's connector for Apache Iceberg, enabling high-performance SQL queries on Iceberg tables.
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Data Governance and Catalogs

Governance and catalog tools provide metadata management, data discovery, and fine-grained access control, ensuring data is secure and easily discoverable.

Hive Metastore

apache/hive
Centralized metadata repository for managing table schemas and partition metadata.
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Apache Atlas

apache/atlas
Open source metadata and governance platform for data cataloging, lineage, and policy enforcement.
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Amundsen

amundsen-io/amundsen
Data discovery and metadata platform for improving productivity and data governance.
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Streaming and Real-Time Processing

Streaming frameworks bring real-time data ingestion, transformation, and analytics to the lakehouse, supporting up-to-date insights.

Apache Flink

apache/flink
Stream processing framework for high-throughput, low-latency data analytics.
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Spark Structured Streaming

apache/spark/tree/master/sql/core/src/main/scala/org/apache/spark/sql/streaming
Built-in streaming analytics engine in Apache Spark, supporting end-to-end exactly-once semantics.
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Data Versioning and Lineage

Tools for data version control and lineage tracking ensure reproducibility, auditing, and compliance within the lakehouse.

lakeFS

treeverse/lakeFS
Git-like data version control for data lakes, enabling safe experimentation and rollbacks.
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DataHub

acryldata/datahub
A modern data catalog that provides metadata management, lineage, and data discovery.
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Orchestration and Workflow Management

Workflow orchestration tools manage complex ETL pipelines and ensure dependable, automated data movement in the lakehouse.

Apache Airflow

apache/airflow
Platform for programmatically authoring, scheduling, and monitoring workflows.
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Dagster

dagster-io/dagster
Data orchestrator for machine learning, analytics, and ETL.
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Dive deeper into the Data Lakehouse Stack by exploring these open source repositories on GitHub. Click on each URL to view the latest releases, community updates, and feature enhancements. Stay current and unlock the full potential of your data lakehouse architecture today!