Dataform Zero to Hero: Master Enterprise-Level Analytics

In this lesson, we break down why traditional SQL pipelines quickly become unmanageable as complexity grows. You’ll see a real-world comparison between spaghetti-style queries and a modular setup using Dataform — and understand why modular analytics is the only way to scale confidently.

In this lecture, you'll learn how to properly set up your Dataform environment inside BigQuery Studio. We’ll walk through creating a repository, setting up individual workspaces for analysts, initializing your project, and configuring the workflow_settings.yaml file. By the end, you'll know how to structure your pipeline, align your dataset locations, and prep your repo for modular development — all without connecting to Git just yet.

In this lesson, we introduce Dataform — a workflow management layer built on top of BigQuery. You’ll learn what makes it different from saved queries or notebooks, and why it’s designed for scalable, team-based analytics work. We’ll walk through the code editor, explain key features like SQLX, assertions, scheduling, and version control, and explore the lineage view to understand how everything fits together.

In this lesson, you’ll learn the fundamentals of version control and why GitHub is essential for modern analytics workflows. We’ll break down key concepts like branches, commits, and pull requests — and show you how to set up your own GitHub account and repository from scratch.

If your company uses an enterprise version of GitHub, make sure to follow your internal setup process.

Note: In an upcoming lecture, we’ll walk through how to connect GitHub with Dataform directly.

In this lesson, you'll learn how to fully connect a GitHub repository to your Dataform project — including authentication, secrets, permissions, and the first push.This is a follow-along tutorial. Make sure to pause the video as needed while completing each step.

We also recommend reviewing the official Dataform documentation for third-party Git provider setup:

In this lesson, we set up a local version of the thelook_ecommerce dataset so you can follow along from any region — including EU-based environments.

You’ll snapshot the public tables into your own BigQuery project, and get a quick overview of what each table contains.

This step is essential before we start building in Dataform.

In this lesson, you’ll learn how to structure your Dataform repo for clarity, modularity, and long-term maintainability.
We’ll cover best practices for organizing folders, grouping SQLX files, using config blocks, and understanding the five core SQLX model types: view, table, incremental, operation, and assertion.

You’ll walk away with a clear blueprint for building scalable pipelines from day one.

Learn how to declare external source tables and reference them using ref() in Dataform.
We'll use JavaScript to bulk-register tables and show how it powers the DAG and dependency tracking. You'll also see how ref() connects everything together in a modular, traceable way.

⚠️ Don’t worry — you don’t need to know JavaScript for this. We provide a ready-to-use snippet you can copy and adapt.

const tables = [

  { name: "orders", database: "your_project", schema: "your_schema" },

  { name: "users", database: "your_project", schema: "your_schema" },

  { name: "products", database: "your_project", schema: "your_schema" }

];

function generateTableDeclarations() {

  tables.forEach(table => {

    declare({

      type: "declaration",

      name: table.name,

      schema: table.schema,

      database: table.database

    });

  });

}

generateTableDeclarations();

In this lecture, you’ll build a modular sales funnel report using BigQuery Dataform.

You’ll write .sqlx files for each step, reuse declared sources with ref(), and follow GitHub workflows for branching and merging. Full hands-on challenge with real pipeline logic.

This video walks through the full solution for all five Dataform challenges, step by step.
We cover every model from user behavior to the final sales funnel summary, explaining the logic, joins, and structure behind each one.

If you’ve already completed the individual assignments, this lecture is optional — but it’s a great way to review, reinforce, or catch anything you missed.

Now that you’ve completed all five challenge assignments, it’s time to see the results in action. In this lecture, you’ll learn how to preview your models using Run, execute the full pipeline with proper dependency handling, and verify everything in BigQuery. This is where your modular analytics stack comes to life.

Now that you’ve executed your full pipeline, it’s time to make it more flexible. In this lecture, you’ll learn how to define shared variables in JavaScript and reference them across your models. This makes it easier to manage filters, constants, and logic without editing multiple files.

Learn how to add data quality checks to your models using assertions in Dataform. You'll implement non-null checks and duplicate detection, run them directly, and inspect results in BigQuery. By the end of this lecture, you'll know how to catch silent data issues without interrupting pipeline execution.

Now that your pipeline is complete, it’s time to move from development to production. In this lecture, you’ll configure a production release that pulls from the main branch, runs on a schedule, and writes to cleanly prefixed prod_ tables. You’ll also learn how to manually compile, trigger executions, and verify your outputs in BigQuery — setting the foundation for a stable, automated workflow.

In this lecture, you’ll connect your BigQuery tables to Power BI and start building dashboards on top of your Dataform pipeline. You'll walk through authentication, project selection, and table import — including how to locate your GCP project ID. By the end, you’ll have a live BI layer powered by automated, production-grade data.