Unlock the full potential of your data workflows with the Data Engineering Stack built upon Apache Spark, Apache Beam, and Apache Flink. This robust stack offers unparalleled scalability, versatility, and performance for constructing efficient ETL pipelines, stream and batch processing, and advanced analytics. Dive into the leading open source projects and their rich ecosystems to accelerate your data-driven initiatives and stay ahead in the world of big data engineering.
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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apache/
airflow

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apache/
beam

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apache/
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apache/
hudi

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apache/
iceberg

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apache/
kafka

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apache/
nifi

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apache/
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apache/
spark

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dagster-io/
dagster

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dbt-labs/
dbt

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debezium/
debezium

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delta-io/
delta

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grafana/
grafana

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great-expectations/
great_expectations

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meltano/
meltano

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prometheus/
prometheus

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tensorflow/
tfx

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

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

Staying up-to-date with latest features of the
Data Engineering 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 Engineering Stack featuring Apache Spark, Apache Beam, and Apache Flink empowers organizations to process, transform, and analyze massive datasets in real-time or batch environments. This stack is essential for building scalable data pipelines, enabling high-throughput data ingestion, transformation, and seamless integration with various data storage and analytics platforms. Leveraging these cutting-edge open source technologies ensures flexibility, reliability, and efficiency in modern data engineering workflows, making it the preferred choice for enterprises and startups alike.

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

Core Processing Engines

These are the foundational distributed data processing frameworks that power the Data Engineering Stack. They enable scalable, fast, and fault-tolerant computation for both batch and stream data processing.

Apache Spark

apache/spark
Unified analytics engine for large-scale data processing with built-in modules for SQL, streaming, machine learning, and graph processing.
what's new in Apache Spark?
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new features in Apache Spark?

Apache Flink

apache/flink
Framework and distributed processing engine for stateful computations over unbounded and bounded data streams.
what's new in Apache Flink?
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new features in Apache Flink?

Apache Beam

apache/beam
Unified programming model for batch and streaming data processing, with execution support across multiple engines.
what's new in Apache Beam?
how to track latest features in Apache Beam?
new updates in Apache Beam?
new features in Apache Beam?

Data Ingestion & Connectors

Tools and libraries for ingesting data from various sources into processing frameworks, supporting integration with message queues, databases, and filesystems.

Apache Kafka

apache/kafka
Distributed event streaming platform capable of handling trillions of events per day, commonly used for real-time data ingestion.
what's new in Apache Kafka?
how to track latest features in Apache Kafka?
new updates in Apache Kafka?
new features in Apache Kafka?

Debezium

debezium/debezium
Open source platform for change data capture (CDC), providing real-time streaming of database changes.
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new features in Debezium?

Apache NiFi

apache/nifi
Powerful data integration tool for automating data flow between systems with a user-friendly UI.
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new features in Apache NiFi?

Data Storage & Lakehouses

Open source solutions for scalable, reliable, and high-performance storage used in conjunction with Spark, Flink, and Beam for analytical workloads.

Apache Hudi

apache/hudi
Upsert, delete, and incremental data processing on large datasets, providing stream processing capabilities on data lakes.
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new features in Apache Hudi?

Delta Lake

delta-io/delta
Storage layer that brings ACID transactions to Apache Spark and big data workloads.
what's new in Delta Lake?
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new features in Delta Lake?

Apache Iceberg

apache/iceberg
High-performance table format for huge analytic datasets, supporting schema evolution and ACID transactions.
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new features in Apache Iceberg?

Orchestration & Workflow Management

Tools for scheduling, orchestrating, and monitoring data pipelines and workflows in the Data Engineering Stack.

Apache Airflow

apache/airflow
Platform to programmatically author, schedule, and monitor workflows and data pipelines.
what's new in Apache Airflow?
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new features in Apache Airflow?

Dagster

dagster-io/dagster
Data orchestrator for machine learning, analytics, and ETL, designed for productivity.
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new features in Dagster?

Data Transformation & ETL

Libraries and frameworks that simplify data transformation and ETL processes for batch and streaming data.

dbt (data build tool)

dbt-labs/dbt
Enables analytics engineers to transform data in their warehouse more effectively with SQL-based workflows.
what's new in dbt (data build tool)?
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new updates in dbt (data build tool)?
new features in dbt (data build tool)?

Meltano

meltano/meltano
Open-source data integration platform that brings together ELT pipelines, orchestrators, and connectors.
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new updates in Meltano?
new features in Meltano?

Monitoring & Observability

Crucial tools for ensuring pipeline health, tracking metrics, and diagnosing issues in distributed data systems.

Prometheus

prometheus/prometheus
Open-source systems monitoring and alerting toolkit widely used for monitoring distributed data platforms.
what's new in Prometheus?
how to track latest features in Prometheus?
new updates in Prometheus?
new features in Prometheus?

Grafana

grafana/grafana
Open-source platform for monitoring, visualization, and observability of metrics and logs.
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new updates in Grafana?
new features in Grafana?

Data Quality & Validation

Open source libraries designed to help data engineers ensure the integrity and quality of data in their pipelines.

Great Expectations

great-expectations/great_expectations
Leading open source tool for data quality, testing, and documentation.
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new features in Great Expectations?

Machine Learning Integration

Frameworks and libraries that integrate with Spark, Beam, and Flink to enable large-scale machine learning workflows.

Apache Spark MLlib

apache/spark/tree/master/mllib
Scalable machine learning library built on top of Apache Spark.
what's new in Apache Spark MLlib?
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TensorFlow Extended (TFX)

tensorflow/tfx
End-to-end platform for deploying production ML pipelines, integrates well with Beam.
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Explore the latest releases and updates for these powerful data engineering repositories by visiting their GitHub pages. Click on the provided URLs to stay current and supercharge your data engineering stack with the best open source technologies available.