This release focuses on some of the harder Kafka architectures: stretched clusters, multi-region setups, failover, MirrorMaker 2, and disaster recovery. It also adds track 13, with nine scenarios driven directly from the terminal. That makes 20 new scenarios in total and brings the curriculum to 100 of 134.
Source: monedula.dev
Apache Kafka is a beautiful system. It scales well, it is stable and it provides phenomenal system architecture flexibility. After 5 years of running production Kafka clusters, I have collected a list of tips and pitfalls.
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Compare Kafka and Iggy architectures, durability guarantees, and benchmarks to understand which streaming engine fits your workload before you decide. At first glance, this looks like a fairly simple comparison. Both systems persist messages in partitioned, append-only logs. Both have offsets, retention policies, consumer groups, and replay. Apache Iggy is written in Rust with a thread-per-core architecture on top of io_uring, while Kafka runs on the JVM. So perhaps we should run a benchmark and pick the faster one.
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