Event Sourcing

Definition

Event Sourcing coverage in this archive spans 3 posts from Apr 2017 to Oct 2021 and centers on data correctness and operability under real production constraints. The strongest adjacent threads are architecture, cqrs, and golang. Recurring title motifs include event, sourcing, practice, and learned.

Working claims

• The common theme is that schema, ownership, and query shape drive most downstream outcomes.
• The consistent theme from 2017 to 2021 is disciplined execution over hype cycles.
• This topic repeatedly intersects with architecture, cqrs, and golang, so design choices here rarely stand alone.

How to apply this

• Define freshness, correctness, and latency targets before choosing storage or pipeline patterns.
• Start with the newest post to calibrate current constraints, then backtrack to older entries for first principles.
• When boundary questions appear, cross-read architecture and cqrs before committing implementation details.

Where teams get burned

• Scaling pipelines before locking down source-of-truth and reconciliation behavior.
• Optimizing single queries while ignoring data model drift and access patterns.
• Applying guidance from 2017 to 2021 without revisiting assumptions as context changed.

Suggested reading path

• Start here (current state): Event Sourcing in Practice: What I Learned Building Financial Event Pipelines
• Then read (operating middle): Event Sourcing in Practice: What I Got Right and Wrong
• Finish with (foundational context): Why We Went Event-Driven (and What Nearly Broke)

Related posts

• Event Sourcing in Practice: What I Learned Building Financial Event Pipelines
• Event Sourcing in Practice: What I Got Right and Wrong
• Why We Went Event-Driven (and What Nearly Broke)

References

• The Twelve-Factor App
• Google SRE Book
• RFC 9110: HTTP Semantics