I haven't dug deep into this project, so take this with a grain of salt.

ArkFlow is a "stateless" stream processor, like vector or benthos (now Redpanda Connect). These are great for routing data around your infrastructure while doing simple, stateless transformations on them. They tend to be easy to run and scale, and are programmed by manually constructing the graph of operations.

Arroyo (like Flink or Rising Wave) is a "stateful" stream processor, which means it supports operations like windowed aggregations, joins, and incremental SQL view maintenance. Arroyo is programmed declaratively via SQL, which is automatically planned into a dataflow (graph) representation. The tradeoff is that state is hard to manage, and these systems are much harder to operate and scale (although we've done a lot of work with Arroyo to mitigate this!).

I wrote about the difference at length here: https://www.arroyo.dev/blog/stateful-stream-processing

No? Vector is for observability, to get your metrics/logs, transform them if needed, and put them in the necessary backends. Transformation is optional, and for cases like downsampling or converting formats or adding metadata.

ArkFlow gets data from stuff like databes and message queues/brokers, transforms it, and puts it back in databases and message queues/brokers. Transformation looks like a pretty central use case.

Very different scenarios. It's like saying that a Renault Kangoo is a simplified equivalent of a BTR-80 because both have wheels, engine and space for stuff.

I worked on stream processing, it was fun, but I also believe it was over-engineered and brittle. The customers also didn't want real-time data, they looked at the calculated values once a week, then made decisions based on that.

Then, I joined another company that somehow had money to pay 50-100 people, and they were using CSV, sh scripts, batch processing, and all that. It solved the clients' needs, and they didn't need to maintain a complicated architecture and the code that could have been difficult to reason about otherwise.

The first company with the stream processing after I left, was bought by a competitor at fire sale price, some of the tech were relevant for them, but the stream processing stuff was immediately shut down. The acquiring company had just simple batch processing and they were printing money in comparison.

If you think it's still worth going with stream processing, give your reasoning to the team, and most reasonable developers would learn it if they really believe it's a significantly better solution for the given problem.

Not to over-simplify, but if you can't convince 5 out of 10 people to learn to make their job better, it's either that the people are not up to the task, or you are wrong that stream processing would make a difference.

Can you help me understand how this would plug into stream processing? My immediate thought is for web page interaction replays — but that seems sort of exotic a use case?