fast-data-dev VS fastkafka

I liked https://lenses.io/ Lots of capabilities but it's not free as I know.

Lenses

For Kafka Dev I use https://github.com/lensesio/fast-data-dev

Currently I'm using Lenses:https://lenses.io/ as UI tool, but while turning on kafka-start-server server.properties and launch the UI on localhost, it failed to connect:

Oh thats sad to hear ... lenses.io is so powerful I am not sure how I would have gotten by to this point without it!

In Addition to the more critical replys from others, why Kafka still makes sense imo: - you could organist everything with custom pipeline/services at your scale. The cost of maintaining those and keep the technology up to date is growing exponentially with every new Service. Kafka offers ansinge Plattform with a couple components which need to be kept up to date. - deploying, managing and monitoring those service is also a big task and does not come for free. - unless your using Kubernetes underneath, scaling is not easy with services. - peer to peer communications of microservices should be avoided because your building a „deathstar“-like architecture where everything is linked and effectively you’ll end up with a distributed monolith. A message broker or message queue helps to avoid this, Kafka is a good tool for that. - I heard that those aws Kafka replacements are not 100% Kafka as one might expect, you should be careful there. But confluent or other providers probably offer hosted solutions on aws too. - https://lenses.io/ helped us a lot when we started with Kafka to see how everything works together, create stream processor’s with sql and easily deploy Kafka connect workers. The tool costs money though but is worth it in the end in my opinion. (Disclaimer: I don’t work for confluent or lenses, we’re running open source Kafka with strimzi and lenses)

https://github.com/lensesio/fast-data-dev is a really cool option for a Kafka development environment based on docker and/or kubernetes.

Yes: - https://lenses.io/ (use the Lenses box, it’s free) - https://github.com/lensesio/fast-data-dev - https://github.com/provectus/kafka-ui

Our journey with FastStream started when we needed to integrate our machine learning models into a customer's Apache Kafka environment. To streamline this process, we created FastKafka using AIOKafka, AsyncAPI, and asyncio. It was our first step in making message queue management easier.

After a short discussion, we concluded we were just too spoiled to use low-level libraries that were nothing more than just tiny wrappers around C++ libs and that we could just build our own. So, we shamelessly made one by reusing beloved paradigms from FastAPI and we shamelessly named it FastKafka. The point was to set the expectations right - you get pretty much what you would expect: function decorators for consumers and producers with type hints specifying Pydantic classes for JSON encoding/decoding, automatic message routing to Kafka brokers and documentation generation.

FastStream simplifies the process of writing producers and consumers for message queues, handling all the parsing, networking and documentation generation automatically. It is a new package based on the ideas and experiences gained from FastKafka and Propan. By joining our forces, we picked up the best from both packages and created a unified way to write services capable of processing streamed data regardless of the underlying protocol. We'll continue to maintain both packages, but new development will be in this project.

FastStream (https://github.com/airtai/faststream) is a new Python framework, born from Propan and FastKafka teams' collaboration (both are deprecated now). It extremely simplifies event-driven system development, handling all the parsing, networking, and documentation generation automatically. Now FastStream supports RabbitMQ and Kafka, but supported brokers are constantly growing (wait for NATS and Redis a bit). FastStream itself is a really great tool to build event-driven services. Also, it has a native FastAPI integration. Just create a StreamRouter (very close to APIRouter) and register event handlers the same with the regular HTTP-endpoints way:

Inspired by FastAPI, FastKafka uses the same paradigms for routing, validation, and documentation, making it easy to learn and integrate into your existing streaming data projects. Please check out the latest version adds supporting the newly released Pydantic v2.0, making it significantly faster. https://github.com/airtai/fastkafka