A curated list of awesome streaming frameworks, applications, readings, and resources for stream processing.
Awesome Streaming is a curated list of resources focused on stream processing frameworks, applications, and tools. It aggregates open-source projects, libraries, and readings to help developers and data engineers navigate the ecosystem of real-time data processing technologies. The list covers streaming engines, data pipelines, IoT solutions, and machine learning libraries for streaming data.
Data engineers, software architects, and developers building real-time data processing systems or evaluating streaming technologies. It's also valuable for researchers and students learning about distributed stream processing.
It saves time by providing a centralized, well-organized directory of streaming resources, eliminating the need to scour multiple sources. The list is community-maintained and includes a wide range of technologies, from established frameworks like Apache Flink to newer projects like Arroyo and RisingWave.
a curated list of awesome streaming frameworks, applications, etc
Open-Awesome is built by the community, for the community. Submit a project, suggest an awesome list, or help improve the catalog on GitHub.
Curates a wide range of streaming engines, libraries, applications, and readings in one place, as evidenced by the detailed table of contents covering from Apache Flink to IoT tools.
Organizes entries into clear sections like Streaming Engine, Data Pipeline, and Streaming SQL, making it easy to browse specific technology types without sifting through clutter.
Lists projects built with various programming languages and stacks, including Java, Scala, Python, Rust, and C++, reflecting the breadth of the streaming ecosystem.
Goes beyond software tools to include a 'Readings' section with books and articles like 'Streaming Systems' and 'Grokking Streaming Systems' for foundational knowledge.
Offers a web version at manuzhang.github.io/awesome-streaming/ that is updated with latest project additions, ensuring some currency beyond the static GitHub README.
Entries provide only brief descriptions without in-depth analysis, reviews, or usage examples, forcing users to visit external pages for comprehensive information.
Lists projects based on curation without ratings, benchmarks, or comparative insights, leaving users to independently evaluate factors like performance and community support.
As a community-maintained list, it may become outdated if updates lag behind rapid changes in the streaming landscape, despite the dynamic website.
The GitHub README is static with no built-in search, filtering, or sorting capabilities, making it cumbersome to find specific tools among hundreds of entries.