Microservices, Events, and Breaking the Data Monolith with Kafka
summary
Explains how Kafka becomes the durable backbone for microservices by storing events (hundreds of terabytes on large clusters) that can be replayed to rebuild materialized views like a social timeline, removing cache-staleness and enabling event sourcing and CQRS. Describes Confluent Operator on Kubernetes plus the Container Service Manager broker for Cloud Foundry bindings, mentions Kafka features (replay, stream processing, high-throughput on hundreds of machines) and shows that projections are rebuilt from events so services stay consistent.
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chapters (23)
- 0:00Intro
- 1:00Thank You Manning
- 2:25Introduction
- 3:28Microservices are good
- 5:50Cloud Native Patterns
- 8:10Loose Coupling
- 9:25Request Response
- 13:48Events
- 14:17materialized view
- 14:49data model
- 15:24eventdriven programming
- 16:15shopping cart example
- 17:30materialized view example
- 18:19read optimize view
- 18:56read arrive view
- 19:35microservices and data
- 20:26summary
- 21:06event streaming
- 24:28monolithic architecture
- 26:18Kafka operator
- 29:16Wrapup
- 29:28Questions
- 29:59Source of Truth
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