Build scalable, modular BigQuery pipelines with Dataform. Git-integrated, testable, production-ready workflows.
Transform your analytics workflow from static SQL to production-ready pipelines
Learn what modular analytics means and why it's essential for scalable data workflows.
Build fully version-controlled pipelines using SQLX, GitHub, and BigQuery integration.
Implement assertions for row counts, primary keys, and null checks to ensure data integrity.
Set up prod_ prefixes and automated refresh schedules for production-ready workflows.
Write models using ref() and build complete funnel reports from source to presentation layer.
Connect BigQuery to Power BI and VS Code notebooks for comprehensive analytics.
Advanced SQL patterns and SQLX syntax for modern analytics engineering.
Branch management and collaboration strategies for data teams.
Best practices for directory organization and tagging strategies.
Scheduled releases and automated data refresh workflows.
Start by understanding the principles of modular analytics and why modern data teams have adopted this approach for scalability and maintainability.
Connect GitHub to Dataform and learn how to structure branches for effective team collaboration and development workflows.
Create complete analytics pipelines from source to reporting layer with proper modeling patterns, directory structures, and tagging strategies.
Set up automated refresh schedules, implement data quality checks, and connect your pipelines to BI tools for complete end-to-end analytics.
Ready to move beyond static SQL and build scalable analytics workflows.
Looking to master modern data tooling and enterprise-level best practices.
Preparing for modern data roles with production-ready portfolio projects.
Level up your workflow from static SQL to real production pipelines. Clean, testable, repeatable — and built to scale.
Enroll Now - Only $5Join modern analytics teams using Dataform at scale