Benthos VS Amazon EMR

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 / about 1 month 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: almost 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

This is a miniature OAuth provider implemented in Benthos and Bloblang. It is designed to serve a single OAuth client app and will generate JWT access tokens with limited lifetime. Source: about 1 year ago

There are different ways to implement parallel dataflows, such as using parallel data processing frameworks like Apache Hadoop, Apache Spark, and Apache Flink, or using cloud-based services like Amazon EMR and Google Cloud Dataflow. It is also possible to use parallel dataflow frameworks to handle big data and distributed computing, like Apache Nifi and Apache Kafka. Source: about 1 year ago

I'm going to guess you want something like EMR. Which can take large data sets segment it across multiple executors and coalesce the data back into a final dataset. Source: almost 2 years ago

This is exactly the kind of workload EMR was made for, you can even run it serverless nowadays. Athena might be a viable option as well. Source: almost 2 years ago

Apache Spark is one of the most actively developed open-source projects in big data. The following code examples require that you have Spark set up and can execute Python code using the PySpark library. The examples also require that you have your data in Amazon S3 (Simple Storage Service). All this is set up on AWS EMR (Elastic MapReduce). - Source: dev.to / over 2 years ago

Check out https://aws.amazon.com/emr/. Source: about 2 years ago