beam VS CouchDB

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.

CouchDB is a json based database for simple projects. The fork pouchdb offers lots of support for offline.

Apache CouchDB belongs to the family of NoSQL databases. It is a document store with a strong focus on replication and reliability. One of the most significant differences between CouchDB and a relational database (besides the absence of tables and schemas) is how you query data. Relational databases allow their users to execute arbitrary and dynamic queries via SQL. Each SQL query may look completely different than the previous one. These dynamic aspects are significant for use cases where you work exploratively with your dataset but don't matter as much in a web context. Additionally, defining an index for a specific table is optional. Most developers will define indices to boost performance, but the database does not require it.

I just turns out you can't always do that in a real codebase. For example see here:

  https://github.com/apache/couchdb/blob/23efd8e5b1aa96ef01640fec03a5fedc945ba8b9/src/couch_mrview/src/couch_mrview_http.erl#L228

Example: Apache Cassandra, CouchDB.

For non-SQL-based databases, consider MongoDB, or CouchDB, which are very easy to get started with.

The Apache CouchDB server. It stores Annotation data.

CouchDB

You can implement the sync algorithm from scratch, or you can use tools like CouchDB and turtleDB to help you.