Designing a scalable event-sourced analytics platform
In modern data systems, you often need to collect, process, and query vast streams of events with guarantees around correctness, replayability, and evolving schemas. This tutorial guides you through building a scalable analytics platform using event sourcing, CQRS, and a pragmatic set of engineering practices. You’ll come away with an architecture you can adapt for product analytics, telemetry, or operational metrics, plus concrete code samples and deployment tips.
1) Architectural overview
Event sourcing: model state changes as a sequence of immutable events. This makes it easy to audit, replay, and retroactively fix bugs by reprocessing events.
Command Query Responsibility Segregation (CQRS): separate write models (commands) from read models (queries). This decoupling improves scalability and lets you optimize reads independently.
