Event Sourcing – the what, why & how
summary
Event sourcing is presented as a practical pattern that stores a complete immutable history and gives a built‑in audit log, implemented here using Azure Cosmos DB change feed as the event store. The concrete architecture uses four building blocks—event store, command handlers, read models and reactors—and runs in production with in-memory read models and reactors that handle automation and external calls so the reactors provide an outbox for free. Tradeoffs: eventual consistency, stream growth and more moving parts.
AI-written summary · about
chapters (33)
- 0:00Introduction
- 1:32Event sourcing in event-driven
- 2:58What event sourcing is
- 5:28Aggregates and domain events
- 7:55CQRS in event sourcing
- 9:19Reactors in event-driven
- 11:30Compensating events
- 12:56Business rules and separation
- 16:06Why use event sourcing
- 17:13Never losing data
- 18:58Future questions with event sourcing
- 20:25Task-based interfaces
- 21:51In-memory read models
- 23:19Event sourcing in distributed
- 24:46Event sourcing in modular monoliths
- 25:29Near real-time UIs
- 26:54Audit logs and debugging
- 28:41Challenges of adoption
- 29:24Immutable events
- 30:28Eventual consistency
- 31:53User expectations
- 33:19Event versioning
- 34:23Managing aggregate streams
- 35:52Why fits certain problems
- 36:36Four building blocks
- 37:18Event store methods
- 39:26Aggregates and business logic
- 42:13Read models and CQRS
- 45:07Reactors in event sourcing
- 47:37Simple event sourcing solutions
- 49:21Event storming design
- 50:46Automation with event sourcing
- 51:30Conclusion
related talks
42mImplementing an Event Sourcing Strategy on Azure
20mHow Azure Cosmos DB enables Event Sourcing architecture
52mEvent Sourcing for the Cloud Developer
01h 01mEvent Sourcing for practice
20mHow Azure Cosmos DB enables Event Sourcing architecture and powers e-commerce
56mImplementing an Event Sourcing strategy on Azure
44mEvent Sourcing - You are doing it wrong
52mEvent Sourcing: deep dive