Modern product engineering often prioritizes shipping features quickly, leaving critical actions such as analytics and counters as an afterthought. The result is scattered logic across service layers, redundant queries, and costly performance bottlenecks.

This talk explores how PostgreSQL triggers can offload key computations—like analytics tracking—beyond the request–response cycle, improving both consistency and latency. Through a practical case study of a content platform (articles, comments, likes, and views), we’ll compare traditional approaches with a trigger-driven design.

Attendees will learn why app-layer analytics often fail at scale, how to design AFTER INSERT/DELETE/UPDATE triggers for analytics counters, and how to manage them safely with Alembic migrations. We’ll also discuss caveats, testing strategies, and production lessons learned.

By the end, you’ll see how to let your database take on more responsibility—resulting in analytics systems that are faster, cleaner, and more reliable.