From 14-hour batches and poor documentation to AI-ready data: SafetyCulture's dbt rebuild
SafetyCulture has used dbt since 2020. But the project that grew up during that time had real problems: 14-hour daily batch execution, poor documentation, and questionable data quality, none of which are compatible with AI-powered BI.
We rebuilt the data model from the ground up, focused on quality, performance, and AI readiness. Results: 90% reduction in execution time, AI BI adoption exceeding 50% across the business, and over US$140K in infrastructure cost savings.
This session covers how we approached the rebuild, the decisions that drove the performance gains, and what it actually took to make dbt-powered AI BI work at scale inside a product company.
Check out more sessions
- Hands-on lab
Standardizing insights with the dbt Semantic Layer
Jenna Bushspies / dbt LabsShania Thomas / dbt LabsView session - Breakout session
From prompt to PR: sandbox-validated dbt changes before human review
Niyazulla Khan Pathan / CareemAkash Srivastava / CareemView session - Breakout session
A practitioner's guide to federated dbt Mesh at scale with Data Project Factory
Trang Trinh / KrampView session
