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 / 16 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 / 3 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

I have also a spark cluster created with google cloud dataproc. Source: about 1 year ago

Specifically, we heavily rely on managed services from our cloud provider, Google Cloud Platform (GCP), for hosting our data in managed databases like BigTable and Spanner. For data transformations, we initially heavily relied on DataProc - a managed service from Google to manage a Spark cluster. - Source: dev.to / about 2 years ago

With that, the best way to maximize processing and minimize time is to use Dataflow or Dataproc depending on your needs. These systems are highly parallel and clustered, which allows for much larger processing pipelines that execute quickly. Source: over 2 years ago