Apache Hadoop VS beam

Did you check out tools like https://hadoop.apache.org/ ?

check out the various dockerfiles bundled with hadoop on GitHub. you can point to them from within docker-compose. they haven't been updated in a couple years tho.

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.

There are several frameworks available for batch processing, such as Hadoop, Apache Storm, and DataTorrent RTS.

A copy of Hadoop installed on each of these machines. You can download Hadoop from the Apache website, or you can use a distribution like Cloudera or Hortonworks.

The Apache™ Hadoop™ project develops open-source software for reliable, scalable, distributed computing.

So, clearly with your AP CS class and PLC logic knowledge, if you were dumped into a codebase like Hadoop, QT, or TensorFlow you'd be able to quickly and competently analyze what is going on with that code, understand all the libraries used, know the reasons why certain compromises were made, and be able to make suggestions on how to restructure the code in a different way? Because I've been programming for coming up on two decades and unless a system is within the domains that I have experience in, I would not be able to provide any useful information without a massive onboarding timeline, and definitely wouldn't be able to help redesign anything until actually coding within the system for a significant amount of time.

This requires the use of distributed computation tools such as Spark and Hadoop, Flink and Kafka are used. But for occasional experimentation, Pandas, Geopandas and Dask are some of the commonly used tools.

The "streaming systems" book answers your question and more: https://www.oreilly.com/library/view/streaming-systems/97814.... 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 https://beam.apache.org very much is. Nowadays people often use "map reduce" as a shorthand for whatever batch processing system they're building on top of.

Apache Beam is one of many tools that you can use

Flexible runtime ☁️: Temporian programs can run seamlessly in-process in Python, on large datasets using Apache Beam.

To perform the tests I'm using a Kafka cluster on Kubernetes from the Beam repo (here).

Apache Beam: Streaming framework which can be run on several runner such as Apache Flink and GCP Dataflow

Apache Beam: Batch/streaming data processing 🔗Link

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.

That being said, there is a learning curve in understanding how Apache Beam works. Take a look at the beam website for more information.