Building A Domain-driven Data Strategy at Checkout.com
summary
Checkout builds domain-oriented data stacks that auto-generate a Snowflake database, compute warehouses, a preconfigured DBT project and an Airflow repo, plus a Monte Carlo domain and Datadog monitors, and already runs about 20 bespoke projects. Feature development uses isolated feature databases seeded from production with a one‑day sample, CI runs DBT tests and schema checks, deployments pause Airflow to avoid conflicts, and cost dashboards attribute Snowflake spend to business units for chargeback.
AI-written summary · about
chapters (10)
- 0:00Introduction to checkout.com
- 0:50Data challenges and strategy
- 3:01Use cases and reporting
- 5:46Organizational models
- 9:11Data platform architecture
- 10:49Technical stack and pipelines
- 13:30Data warehouse organization
- 15:00Self-service data factory
- 19:44Automation, governance, and monitoring
- 25:15Building data communities
related talks
39mBuilding a Modern Data Warehouse Without Breaking the Bank
21mSnowflake and dbt -- Our Journey to the Cloud
43mData Modelling for Startups: Domain Models & the Modern Data Stack
35mBuilding a data analytics platform on strong opinions held loosely
01h 17mData Engineering in the AI Era: Building a Robust Foundation for AI Applications
30mBuilding Data AI Application with the Snowflake AI Data Cloud
19mRobust CI Workflow with dbt for Data Warehouse Optimization
23mData Team as an Optimisation Problem