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This course teaches you how to build clean, modular, and scalable analytics pipelines using Dataform on BigQuery. It's the same workflow used by modern analytics teams at scale.You'll start by learning what modular analytics actually means and why it matters. Then, you'll build a fully version-controlled pipeline using SQLX, GitHub, and BigQuery - from source to reporting layer.We'll guide you through modeling patterns, directory structures, tagging strategies, assertions, and release scheduling. You'll write models using ref(), build a full funnel report, validate data quality, and trigger scheduled runs from the main branch.You'll also learn how to:Connect GitHub to Dataform and structure branches for collaborationUse assertions for row count, primary key, and null checksSet up prod_ prefixes for your production tablesAutomatically refresh outputs on a release scheduleConnect BigQuery to Power BI and optionally to VS Code notebooks for local developmentBy the end, you'll have a complete analytics stack that's clean, testable, repeatable - and built to scale.If you're a data analyst, analytics engineer, or job seeker preparing for a modern data role, this course will level up your workflow from static SQL to real production pipelines and modernize your entire approach to analytics.

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