Scaling Metrics at Airbnb 🏠, Automated dbt Docs 📚, Postgres Queue Pitfalls 🧹

**TL;DR** - How Airbnb built its metrics infrastructure to scale across 7M listings; automated dbt documentation generation; Postgres queue pitfalls in high-throughput systems.

The 10-Second Pitch

• Airbnb metrics platform handles data from 7M listings, 150M users, and 100K+ hosts with sub-second query response
• dbt doc automation now good enough to replace manually maintained data dictionaries
• Postgres queue patterns (SKIP LOCKED, advisory locks) underutilized for high-throughput job processing

Setup in 3 Steps

1. Evaluate dbt doc automation for your next data documentation sprint - saves significant maintenance overhead

2. If building job queues in Postgres, use SKIP LOCKED instead of SELECT FOR UPDATE

3. Study Airbnb metrics architecture - their open-source tooling (Superset, Dataportal) worth evaluating

**Example Prompt:**

Design a Postgres-based job queue handling 10K jobs per minute with exactly-once semantics.

Verdict

SKIP LOCKED is most underrated Postgres feature nobody uses. If polling a queue table, you should be using it.