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Some folks wonder whether @ApacheKafka is "worth it at their scale". But solely focusing on message count and through-put means to miss out on many other interesting characteristics of Kafka. Here here are just three which make it useful for all kinds of deployments (1/5):
* Fault-tolerance and high availability; topics can be replicated, consumers can fail-over -- Machines will fail, programs will crash, being able to mitigate this is always of value, no matter the scale of an application (2/5)
* Messages can be retained for a potentially indefinite time, consumers are in full control from where they read a topic -- Comes in handy to re-process some messages or entire topics, e.g. after failures, or for bringing in new consumers of existing messages (3/5)
* There's a rich eco-system of source and sink connectors for getting data into and out of Kafka -- Enabling low-latency data pipelines in a purely declarative way, e.g. for pushing data via change data capture from your operational database to search indexes, caches or DWH (4/5)
And there's much more, e.g. Kafka Streams for enriching event streams, calculating aggregations as your data changes, and surely things I've forgotten. But I hope it helps to convince folks that Kafka isn't only useful for large-scale use cases. (Fin)
