Beyond the Hype: When Apache Flink Solves Real Problems

on YouTube ↗41m

" summary

When Flink becomes essential: event-driven fraud detection, CDC that syncs RDBMS to search in sub-second, and low-latency dashboards where freshness matters; concrete contrasts include Kafka Streams = JVM library, Kafka-native, no SQL, ksqlDB = SQL on Kafka with its own cluster, and Spark Structured Streaming as micro-batch with limited stateful/event-time features. Flink supplies event-time processing, watermarks for out-of-order and late arrivals, exactly-once guarantees, Table API/Flink SQL, Kubernetes deployment, 100+ connectors and fits a streamhouse pattern for real-time OLAP plus longer-term analytical storage.

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" chapters (14)

1. 0:00Introduction
2. 1:04Why streaming matters
3. 3:05Core streaming use cases
4. 5:11Streaming platform landscape
5. 7:26Stream processing entities
6. 10:35KSQL DB and streaming databases
7. 13:21Streaming databases and patterns
8. 16:47Real-time OLAP engines
9. 19:13Stream processing frameworks
10. 21:07Apache Flink overview
11. 23:58Hard problems in streaming
12. 26:43Streamhouse architecture
13. 31:05Conclusion and takeaways
14. 33:49Q&A session

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