Explore the Data Lake Stack (AWS S3, Apache Spark, Apache Hive) stack: a robust framework combining cloud-native object storage, distributed computation, and flexible data warehousing. With AWS S3, Apache Spark, and Apache Hive at its core, this stack empowers enterprises with scalable storage, lightning-fast analytics, and rich ecosystem support for modern data lake architectures.
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.

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dask/
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delta-io/
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getredash/
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prestodb/
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streamsets/
datacollector

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trinodb/
trino

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

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Staying up-to-date with latest features of the
Data Lake 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 Lake Stack (AWS S3, Apache Spark, Apache Hive) stack offers a powerful, scalable, and cost-effective solution for managing big data analytics. Leveraging the durability and scalability of AWS S3, the distributed processing capabilities of Apache Spark, and the flexible data warehousing features of Apache Hive, this stack enables businesses to store, process, and analyze massive volumes of structured and unstructured data efficiently. It supports seamless data ingestion, high-performance querying, and integration with a broad ecosystem of open source tools, making it ideal for data engineering, ETL, and advanced analytics workflows.

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

Core Data Lake Libraries

These foundational libraries power the key components of the data lake stack, enabling scalable storage, distributed computation, and efficient querying. They form the backbone of any modern data lake solution.

Apache Spark

apache/spark
Unified analytics engine for large-scale data processing, featuring high-level APIs and support for SQL, streaming, and machine learning.
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Apache Hive

apache/hive
Data warehouse software facilitating reading, writing, and managing large datasets residing in distributed storage using SQL.
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Hadoop Common

apache/hadoop
Common utilities that support other Hadoop modules, crucial for integrating with Hadoop-compatible file systems like S3.
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Cloud Storage Integration

Libraries and connectors that enable seamless interaction between big data processing frameworks and cloud-native storage solutions such as AWS S3.

Hadoop AWS

apache/hadoop/tree/trunk/hadoop-tools/hadoop-aws
A library for Hadoop that provides connectors to AWS S3, allowing Hadoop and its ecosystem tools to read and write data to S3 buckets.
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s3fs

dask/s3fs
Pythonic file interface to S3, used for reading and writing S3 data easily in data engineering workflows.
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AWS SDK for Java

aws/aws-sdk-java
Official AWS SDK enabling Java applications, including Spark and Hive, to interact with AWS resources such as S3.
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Data Lake Table Formats

Modern open-source table formats designed for big data analytics, providing ACID transactions, schema evolution, and time travel capabilities.

Apache Hudi

apache/hudi
Open-source transactional data lake platform enabling stream processing and incremental data pipelines on S3.
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Apache Iceberg

apache/iceberg
High-performance table format for huge analytic datasets, supporting schema evolution and partitioning.
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Delta Lake

delta-io/delta
Storage layer that brings ACID transactions to Apache Spark and big data workloads, supporting scalable data lakes.
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Data Ingestion and ETL

Tools for ingesting, transforming, and loading data into the data lake from a variety of sources, supporting batch and streaming pipelines.

Apache NiFi

apache/nifi
Data integration platform delivering easy-to-use, powerful, and reliable data ingestion and distribution across systems.
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Apache Airflow

apache/airflow
Workflow orchestration platform for programmatically authoring, scheduling, and monitoring complex data pipelines.
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StreamSets Data Collector

streamsets/datacollector
Open source platform for building and operating continuous data ingestion pipelines.
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Data Catalog and Metadata Management

Solutions for discovering, cataloging, and governing data assets in the data lake, ensuring data quality and compliance.

Apache Atlas

apache/atlas
Open source metadata and governance platform for managing data assets and lineage in big data ecosystems.
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Amundsen

amundsen-io/amundsen
Metadata-driven data discovery and catalog platform for improving data accessibility and collaboration.
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Query Engines

High-performance distributed SQL engines for interactive and batch querying of data stored in the data lake.

PrestoDB

prestodb/presto
Distributed SQL query engine for big data, designed for fast analytics on large datasets stored in data lakes.
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Trino

trinodb/trino
Fast distributed SQL query engine for big data analytics, formerly known as PrestoSQL.
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Data Visualization and Exploration

Open source tools for exploring, visualizing, and analyzing data residing in the data lake, enabling better business insights.

Apache Superset

apache/superset
Modern data exploration and visualization platform, offering interactive dashboards and charts for data lake analytics.
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Redash

getredash/redash
Open-source tool for visualizing and sharing data from various sources including data lakes.
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Data Security and Governance

Libraries and frameworks to secure, audit, and govern access to sensitive data in the data lake environment.

Apache Ranger

apache/ranger
Framework to enable, monitor, and manage comprehensive data security across the Hadoop ecosystem.
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Discover the latest features, improvements, and innovations in the Data Lake Stack (AWS S3, Apache Spark, Apache Hive) stack by visiting the repositories listed above. Click on each URL to explore the official releases, detailed documentation, and community contributions for this powerful data lake architecture.