The "streaming systems" book answers your question and more: https://www.oreilly.com/library/view/streaming-systems/9781491983867/. It gives you a history of how batch processing started with MapReduce, and how attempts at scaling by moving towards streaming systems gave us all the subsequent frameworks (Spark, Beam, etc.). As for the framework called MapReduce, it isn't used much, but its descendant... - Source: Hacker News / 4 months ago

Apache Beam is one of many tools that you can use. Source: 6 months ago

Apache Beam: Streaming framework which can be run on several runner such as Apache Flink and GCP Dataflow. - Source: dev.to / over 1 year ago

Apache Beam: Batch/streaming data processing 🔗Link. - Source: dev.to / almost 2 years ago

What you are looking for is Dataflow. It can be a bit tricky to wrap your head around at first, but I highly suggest leaning into this technology for most of your data engineering needs. It's based on the open source Apache Beam framework that originated at Google. We use an internal version of this system at Google for virtually all of our pipeline tasks, from a few GB, to Exabyte scale systems -- it can do it all. Source: almost 2 years ago

Streaming and transforming structured documents at scale used to require some awfully complex machinery such as Apache Camel, Kafka Connect, Flink, etc. I was so happy when I bumped into Benthos https://benthos.dev which can be used as a lightweight replacement in most cases. Bonus: It’s written in Golang, so I don’t have to bother with heavy dependencies and slow start times. - Source: Hacker News / 13 days ago

If you're interested in Golang and data streaming, https://benthos.dev is a good project to contribute to. There are quite a few issues open on the GitHub project which anyone can pick up. Writing new connectors and adding tests / docs is always a good place to start. The maintainer is super-friendly and he's always active on the https://benthos.dev/community channels. I'm also there most of the time, since I've... - Source: Hacker News / 2 months ago

I have been working in the stream processing space since 2020 and I used Benthos. Since Benthos is a stateless stream processor, I have other components around it which deal with various types of application state, such as Kafka, NATS, Redis, various flavours of SQL databases, MongoDB etc. Source: about 1 year ago

You might want to add Benthos to your stack. It’s Open Source and it works great for data streaming tasks. You could have your task orchestrator (Airflow, Flyte etc) run it on demand. I demoed it at KnativeCon last year. Source: about 1 year ago

A few years ago, I found Benthos (the Open Source data streaming processor) and it was really easy to dive into it and add new features. Going through the various 3rd party libraries that it includes is usually straightforward and I'm comfortable enough with the language and various design patterns now to quickly get what's going on. That was rarely the case with C++. Source: about 1 year ago