## Notes ### Use Cases - Started off as log aggregation in LinkedIn - Multiple producers would write to Kafka, which would then write to logs, ELK - Real-time machine learning pipelines - Aggregate user input, sensor data, financial data, etc to train/infer - Real-time monitoring and alerting - Change detection to replicate data in other datastores, like transaction logs - System migration - Buffer between old and new systems ### Properties - Has built-in persistence to replay events ## Related Technologies - Flink - Kafka Streams - Spark Streaming ## References - [Kafka Use Cases - YouTube](https://www.youtube.com/watch?v=Ajz6dBp_EB4)