Microservices Data Patterns: CQRS & Event Sourcing

on YouTube ↗46m

" summary

Argues that event sourcing gives fast writes, audit trail and time travel but poor reads, so pair it with CQRS read models and selective replication; demonstrates using Debezium (CDC) to tail DB transaction logs, publishing to Kafka for ordered, persistent replication, and a Vert.x reactive gateway that deduplicates 50 concurrent callers to a third‑party API to cut costs. Recommends in‑memory data grids for low‑latency queries and lists tradeoffs: latency, volume, staleness, ownership, security and retention.

AI-written summary · about

" chapters (13)

1. 0:00Introduction
2. 2:35Data management evolution
3. 4:26Event sourcing explained
4. 9:02CQRS principle
5. 11:12Implementing CQRS
6. 17:51Combining ES and CQRS
7. 20:51Distributed data systems
8. 24:21Replication requirements
9. 29:45In-memory data grids
10. 34:57Data virtualization
11. 37:34Message brokers
12. 40:21Reactive platforms
13. 43:00Change data capture

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