awesome-streaming
github.com/manuzhang/awesome-streaming ↗a curated list of awesome streaming frameworks, applications, etc
Use this list with your AI agent
Add the Context Awesome MCP server to Claude, Cursor, or any MCP client, then ask:
"Show me engines and platforms resources from awesome-streaming"
Installation instructions →What's inside
Engines and Platforms
- Aeron
Reliable UDP unicast, multicast, and IPC message transport.
- Apache Apex
Unified platform for big data stream and batch processing.
- Apache Flink
Distributed engine for stateful computation over bounded and unbounded data streams.
- Apache Heron
Retired distributed, fault-tolerant stream processing engine originally developed at Twitter.
- Apache Pulsar
Distributed pub-sub messaging and event streaming platform.
- Apache RocketMQ
Cloud-native messaging and streaming platform for event-driven applications.
Libraries, SDKs, and Programming Models
- Akka Streams
Reactive Streams implementation built on Akka actors.
- Apache Beam
Unified programming model and language-specific SDKs for batch and streaming data processing.
- Apache Edgent
Retired programming model and runtime for streaming analytics on gateways and edge devices.
- Apache Kafka Streams
Stream processing library included with Apache Kafka.
- Apache Pekko
Open-source toolkit for concurrent, distributed, resilient applications, forked from Akka 2.6.
- Apache SAMOA
Retired distributed streaming machine learning framework.
Managed and Closed Source
- Amazon Kinesis Data Streams
Fully managed service for ingesting and processing real-time data streams on AWS.
- Azure Stream Analytics
Fully managed service for serverless real-time analytics in the cloud and at the edge.
- Concord
Historical distributed stream processing framework built on Apache Mesos.
- Google Cloud Dataflow
Fully managed service for running Apache Beam batch and streaming pipelines.
- IBM Streams
Discontinued proprietary platform for distributed stream processing and real-time analytics.
- NVIDIA DeepStream SDK
GStreamer-based toolkit with open-source components and proprietary NVIDIA libraries for real-time AI streaming analytics and multi-sensor processing.
Data Integration and Pipelines
- Apache Flume
Distributed service for collecting, aggregating, and moving large amounts of log-like data.
- Brooklin
Distributed system for reliable nearline data streaming between heterogeneous systems at scale.
- Bruin
End-to-end data pipeline tool combining ingestion, SQL and Python transformations, and data quality checks.
- Camus
LinkedIn's retired, previous-generation Kafka-to-HDFS pipeline.
- CocoIndex
Incremental data transformation engine for continuously updated AI and agent workloads.
- Databus
LinkedIn source-agnostic distributed change data capture system.
Applications and Tools
- beava
Single-binary feature server for querying fresh per-entity counters and aggregates without a message broker.
- Eventum
Data generation platform for producing synthetic event streams.
- javactrl-kafka
Code-first distributed workflow engine for microservice orchestration and business process automation.
- Nussknacker
Visual tool for defining and running real-time decision algorithms.
- straw
Platform for real-time streaming search.
- StreamAlert
Airbnb serverless framework for real-time security log analysis and alerting.
Readings
Benchmarks
- Flotilla
Automated message queue orchestration for scaled-up benchmarking.
- storm-perf-test
Apache Storm performance and stress test.
- streaming-benchmarks
Benchmarks for low-latency stream processing systems including Storm, Spark, and Flink.
Showing a sample of 135 resources. View the full list on GitHub →