Building ETL Pipelines with SQL

This course teaches how to build production-ready ETL pipelines using pure SQL on the Databricks Data Intelligence Platform. Students learn Streaming Tables with Auto Loader for incremental ingestion, Materialized Views with incremental refresh for Silver-to-Gold transformations, AUTO CDC (FLOW AUTO CDC) for declarative SCD Type 1 and Type 2 dimension management, and Lakeflow Jobs with SQL File tasks for production orchestration. The course follows a realistic retail dataset through the medallion architecture (Bronze → Silver → Gold).

Note: Databricks Academy is transitioning to a notebook-based format for classroom sessions within the Databricks environment, discontinuing the use of slide decks for lectures. You can access the lecture notebooks in the Vocareum lab environment.

Skill Level

Associate

Duration

Prerequisites

In this course, the content was developed for participants with these skills/knowledge/abilities:

• Navigating the Databricks workspace (sidebar, Catalog Explorer, SQL Editor)

• Unity Catalog basics (catalogs, schemas, tables, volumes)

• Intermediate SQL (SELECT, JOIN, GROUP BY, CAST, COALESCE, CREATE TABLE)

• Data warehousing concepts (fact/dimension tables, star schemas, medallion architecture)

• Basic understanding of ETL workflows

Outline

SQL ETL on Databricks

• SQL ETL on Databricks: The Big Picture

• Demo - Exploring the Course Dataset and SQL Editor

• Lab - Using the SQL Editor and Genie Code

Streaming Tables and Materialized Views
• Building SQL ETL Pipelines

• Demo - Building a Silver-to-Gold Pipeline

• Lab - Building a Customer Feedback Pipeline

Auto CDC
• AUTO CDC Streaming Dimension Updates

• Demo - Building Slowly Changing Dimensions with AUTO CDC

• Lab - Building Slowly Changing Dimensions

Orchestrating with Lakeflow Jobs
• Orchestrating SQL Pipelines with Lakeflow Jobs

• Demo - Building a Lakeflow Job for the ETL Pipeline

• Lab - Orchestrating SQL Pipelines with Lakeflow Jobs

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You will follow a complete end-to-end workflow, from understanding source data and defining benchmarks to configuring and refining a Genie Space using the full set of Knowledge Store curation tools. These include metadata, synonyms, prompt matching, SQL logic, example queries, and text instructions.

You will also learn how to share Genie Spaces with business users through Databricks One, understand how Unity Catalog governance is automatically enforced, and use monitoring and user feedback to continuously improve quality over time.

By the end of the course, you will be able to create and manage a production-ready Genie Space that delivers governed, self-service conversational analytics at scale.

Note: Databricks Academy is transitioning to a notebook-based format for classroom sessions within the Databricks environment, discontinuing the use of slide decks for lectures. You can access the lecture notebooks in the Vocareum lab environment.

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Languages Available: English | 日本語 | 한국어

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