What if your database worked more like Git? Every change captured as an immutable event you can replay, instead of a single mutating row that quietly forgets its own history. That's event sourcing, and Chris May is back on Talk Python, fresh off our Datastar panel, to walk us through what it actually looks like in Python. We'll cover the core patterns, the libraries to reach for, when not to use it, and why event sourcing turns out to be a surprisingly good fit for AI-assisted coding.

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Talk Python Courses

Links from the show

Chris May: everydaysuperpowers.dev

Intro to event sourcing e-book: everydaysuperpowers.gumroad.com

Domain-Driven Design: The Power of CQRS and Event Sourcing: How CQRS/ES Redefine Building Scalable System: ricofritzsche.me

DDD: www.amazon.com

Understanding Eventsourcing (Martin Dilger): www.amazon.com

Event Sourcing Explained using Football Video: www.youtube.com

Why I finally embraced event sourcing and why you should too article: everydaysuperpowers.dev

valkey: valkey.io

diskcache: talkpython.fm

eventsourcing package: github.com

eventsourcing docs: eventsourcing.readthedocs.io

John Bywater: github.com

Datastar: data-star.dev

Microconf: microconf.com

Event Modeling & Event Sourcing Podcast: podcast.eventmodeling.org

Python Package Guides for AI Agents: github.com

Iodine tablets AI joke: x.com

KurrentDb: www.kurrent.io

Watch this episode on YouTube: youtube.com

Episode #548 deep-dive: talkpython.fm/548

Episode transcripts: talkpython.fm

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Michael on Mastodon: @[email protected]

Michael on X.com: @mkennedy