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ceencee

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ceencee
·il y a 3 ans·discuss
I feel like this is frankly uninformed. Many iceberg shops seem to rely heavily on Spark as a primary engine. And databricks has a history of being a hostile oss force with the culture of the spark project being toxic from the start and delta’s questionable commitment to being a community project.
ceencee
·il y a 3 ans·discuss
Kafka can sustain sub 20ms at millions or even billions per second scale. Processing time delays is bad consumer code and partition design smell. Aka , your consumer shouldnt depend on a slower resource within an ordering domain. This can also be mitigated with an async consumer
ceencee
·il y a 3 ans·discuss
This is a ridiculous statement if you really build an EDA. Kafka is what enables the decoupling.
ceencee
·il y a 3 ans·discuss
Pretty much every bank uses kafka ad the central messaging layer. What people are missing in almost every post here is the write once read many without data duplication and with different offsets is the killer app for Kafka beyond just the near infinite scale which is also super appealing. The failure modes are way way better than Rabbit as well. Note: I owned the streaming platform for a top 5 bank in the us.
ceencee
·il y a 3 ans·discuss
Who is running single az deployments who also cares about data loss and availability? Seriously? I’ve personally supported 1000s of kafka deploys and this isn’t a thing in the cloud at least. There is no call for wanting fsync per message, it is an anti pattern and isn’t done because it isn’t necessary. Data loss in kafka isn't a real problem that hurts real world users at all.