Data Microservices with Spring Cloud

on YouTube ↗47m

" summary

Explains how to build lightweight, data-centric microservices that pass payloads through a durable broker instead of direct REST calls, using Spring Cloud Stream’s binder abstraction to connect to Kafka or RabbitMQ and scale with consumer groups and partitioning; demonstrates composing simple one‑purpose services into pipelines, highlights tradeoffs — eventual consistency, broker ops cost — and shows a concrete architecture using event sourcing plus CQRS with snapshots to reconstruct state and limit replay time.

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" chapters (14)

1. 0:00Introduction to data microservices
2. 1:11What are data microservices
3. 3:38Why focus on data instead of apps
4. 6:09Benefits and drawbacks of data microservices
5. 10:28Spring Cloud Stream overview
6. 11:31Spring Cloud Stream architecture
7. 16:13Scaling with consumer groups
8. 18:18Partitioning for performance
9. 21:50Data consistency challenges
10. 29:47Event-driven alternatives to transactions
11. 31:56Event sourcing pattern explained
12. 35:32CQRS for read/write separation
13. 39:06Summary and key takeaways
14. 41:37Q&A session

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