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cha0sengine

2 karma · joined May 20, 2021

I'm interested in time-series data, MLOps, and all things Kafka. I'm also a full-stack developer at Quix, a real-time telemetry data stack where you can build powerful apps all in Python and C#.
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cha0sengine··on Show HN: An open-source library for telemetry streaming started at Formula 1
Quix Streams was envisioned to support multiple brokers from day one. We've built abstraction on top of Kafka, so it would be possible to integrate with different message brokers. Which broker do you have in mind?
cha0sengine··on Show HN: An open-source library for telemetry streaming started at Formula 1
Hi, I recorded a quick loom here -> https://www.loom.com/share/876f1475113a481b8d2591d0ce2f302c

But basically if you login to the Quix Portal and head to the library you can see a bunch of data sources.

cha0sengine··on Show HN: An open-source library for telemetry streaming started at Formula 1
Well, we go into a bit more detail in the blog post (2nd link above). But essentially there are a lot of custom features that focus on time, specifically time-series data. Also, the stream context feature is quite different, allowing you to send different types of data (time series, events, binary, etc) in one ordered stream, bound to one partition. Regarding Faust specifically, the big difference is in the approach to stateful processing. Faust basically copies Kafka's native KafkaStream's architecture, where state is stored in topics - our library, on the other hand, avoids this by using a K8s persistence volume (shared in a stateful set) to persist state and manage state checkpointing.