conduit VS bytewax

I'd like to mention Conduit + its Postgres connector. The Pg connector comes built-in, so all that is needed is a single Conduit binary to get started. It relies on WAL, but the connector creates the replication slot itself (if needed).

GitHub - ConduitIO/Conduit: Data Integration for Production Data Stores. Conduit is meant to be a bit more general-purpose than Kafka Connect and is an easy drop-in replacement. We're working hard to make that even easier. We are still in the early stages of this project and are trying to build more and more connectors. You can find out more about our connector roadmap on the Github repo. Our connector philosophy is to be real-time first, and double down on change data capture (CDC) capabilities, all with permissive licensing.

If you're looking for a tool with a UI and in which you can also easily extend the functionality with your own, custom data connectors, you might also want take a look at Conduit which is another open-source tool we've developed to make building and running real-time data infrastructure more straightforward and less time consuming.

You might want to check out: https://github.com/conduitio/conduit

This is why we are working on a project called Conduit at Meroxa. We hope to change the experience software engineers have with data.

You can probably use Python to solve your problem, there are many ways you can speed up your deserialization/flattening. I work on Bytewax (https://github.com/bytewax/bytewax) and I wouldn't mention it if it wasn't a good fit, but I think it's worth looking at here. It is a stream processor that makes it easy to scale, maintain order, track progress, and you just write native Python.

Disclaimer: I work on Bytewax, but it feels like this could be a good fit and would save you some time looking around. If you need to do stateful operations (reduce, window, etc.) then you can use bytewax - https://github.com/bytewax/bytewax with pub/sub, but you would need to build a custom connector. There are some guides on how to do that - https://www.bytewax.io/blog/custom-input-connector.

This is great, definitely see the utility here. I have had to hack this together so many times while building streaming workflows with github.com/bytewax/bytewax and other tools.

When low latency matters you should always consider an ETL approach rather than ELT, e.g. collect data in Kafka and process using Kafka Streams/Flink in Java or Quix Streams/Bytewax in Python, then sink it to Snowflake where you can handle non-critical workloads (as is the case for 99% of BI/analytics). This way you can choose the right path for your data depending on how quickly it needs to be served.

Working on how to use https://github.com/bytewax/bytewax to create embeddings in real-time for ML use cases. I want to make a small library for embedding pipelines, but still learning about vector dbs and the tradeoffs between the different solutions.

Project looks cool! Glad you open sourced it. It could use some comments in the code base to help contributors ;). I also like the datafusion usage, that is awesome. BTW I work on github.com/bytewax/bytewax, which is based on https://github.com/TimelyDataflow/timely-dataflow another Rust dataflow computation engine.

Cool, nice idea. Can you sub in different backend like bytewax (https://github.com/bytewax/bytewax) for stateful processing?

If you want to keep in your Python/SQL area of expertise and by all means I don't mean to promote not learning a new language, but just as an FYI. There are some non-Java/Scala tools between streaming databases like risingwave and materialize, streaming platforms like fluvio and redpanda, and stream processors like bytewax and faust.