ElasticSearch, MongoDB and Neo4j walk into a bar a tale of different databases
summary
Claim: capture database writes and stream them into specialized stores so each service gets the view it needs. Implements change data capture using Red Hat’s Debezium → Kafka, then materializes into MongoDB (10-node cluster), Neo4j and Elasticsearch via Kafka Streams, demonstrating denormalized movie documents and graph queries that work. Kafka sustains millions of messages/sec and lets systems rebuild state from the log; joins require local state stores and rebuilding half‑a‑billion rows can take hours. This is an event-driven microservices approach with eventual consistency and replayable data.
AI-written summary · about
chapters (4)
related talks
33mThe rise and fall of the full stack database
42mData Streaming for Microservices using Debezium
39mModern Event-driven Architecture: Adopting Data in Motion
48mStreaming Database Changes with Debezium
32mDiscover the Modern Way to Build Real-Time Analytics Solutions at Any Scale
43mBack to fundamentals: from databases to event-driven platforms
15mManaging Data Consistency among Microservices with Debezium
55mThe hardest part of microservices: Your Data