manuzhang/awesome-streaming

a curated list of awesome streaming frameworks, applications, etc

View on GitHub ↗Jump to charts ↓

manuzhang/awesome-streaming has 3k+ stars and 300+ forks on GitHub, is written in JavaScript, and was last pushed on Oct 17, 2026. Data as of . Signed-in members get hourly updates — create a free account.

Summary Information

Updated 39 minutes ago
Type:Curated List / Learning ResourceCategory(s):Queues, Streaming & EventsData Engineering & Analytics
Added to GitGenius on September 23rd, 2026
Created on December 18th, 2014
Open Issues & Pull Requests: 7 (+0)
GitHub issues: Enabled
Number of forks: 336
Total Stargazers: 3,023 (+0)
Total Subscribers: 131 (+0)

Repository Insights (GitGenius)

Median issue/PR response: 1.7 hours
Mean response time: 7.7 hours
90th percentile: 21.1 hours
Tracked items: 3

Charts & Analytics

Fetching additional details & charts...

Issue Activity (beta)

Open issues: 2
New in 7 days: 1
Closed in 7 days: 0
Avg open age: 353 days
Stale 30+ days: 1
Stale 90+ days: 1

Recent activity

Opened in 7 days: 1
Closed in 7 days: 0
Comments in 7 days: 0
Events in 7 days: 0

Top labels

No label distribution available yet.

Most active issues this week

Sign in to see which issues are moving.
Sign in

Detailed Description

Awesome Streaming is a curated list of streaming frameworks, applications, and resources for stream processing.

The project addresses the challenge of discovering and understanding the landscape of stream processing technologies. Rather than building tools itself, it serves as a comprehensive reference guide that organizes streaming frameworks, libraries, platforms, and related resources into logical categories. This approach helps developers navigate the ecosystem by providing a structured overview of available options across different aspects of stream processing, from core engines and platforms to data integration tools and benchmarking resources.

Developers should use this list when evaluating stream processing solutions or learning about the broader ecosystem. It suits anyone building real-time data pipelines, implementing event-driven architectures, or researching stream processing approaches. The list is organized into distinct sections covering engines and platforms, libraries and SDKs, data integration and pipelines, applications and tools, managed services, benchmarks, and readings, making it useful both as a discovery tool and as a reference for understanding how different components fit together. The project also maintains a dynamic website alongside the repository, providing a more interactive way to explore and stay updated on the listed projects.

The project receives regular updates to keep the curated list current with the evolving stream processing landscape. The repository includes automated build checks to maintain quality and consistency of the list structure.