Apache Beam VS Gearman

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 / 3 months ago

Apache Beam is one of many tools that you can use. Source: 5 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 / over 1 year 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: over 1 year ago

I am a fan of Gearman to schedule and dispatch distributed jobs, Redis as a collaborative blackboard, and GlusterFS to share models across multiple systems and make bulk data available across the entire system (usually referenced in the blackboard as a pathname). Source: 12 months ago

At work we typically use Gearman (http://gearman.org/) or Symfony messenger (https://symfony.com/doc/current/messenger.html) to queue up a batch of jobs. And then we use supervisord (http://supervisord.org/) to keep a pool of PHP processes running to process the jobs. Source: about 2 years ago