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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Upcoming Public Classes

Apache Spark Developer

Apache Spark™ Programming with Databricks

This course serves as an appropriate entry point to learn Apache Spark Programming with Databricks.

Below, we describe each of the four, four-hour modules included in this course.

Introduction to Apache Spark

This course offers essential knowledge of Apache Spark, with a focus on its distributed architecture and practical applications for large-scale data processing. Participants will explore programming frameworks, learn the Spark DataFrame API, and develop skills for reading, writing, and transforming data using Python-based Spark workflows.

Developing Applications with Apache Spark

Master scalable data processing with Apache Spark in this hands-on course. Learn to build efficient ETL pipelines, perform advanced analytics, and optimize distributed data transformations using Spark’s DataFrame API. Explore grouping, aggregation, joins, set operations, and window functions. Work with complex data types like arrays, maps, and structs while applying best practices for performance optimization.

Stream Processing and Analysis with Apache Spark

Learn the essentials of stream processing and analysis with Apache Spark in this course. Gain a solid understanding of stream processing fundamentals and develop applications using the Spark Structured Streaming API. Explore advanced techniques such as stream aggregation and window analysis to process real-time data efficiently. This course equips you with the skills to create scalable and fault-tolerant streaming applications for dynamic data environments.

Monitoring and Optimizing Apache Spark Workloads on Databricks

This course explores the Lakehouse architecture and Medallion design for scalable data workflows, focusing on Unity Catalog for secure data governance, access control, and lineage tracking. The curriculum includes building reliable, ACID-compliant pipelines with Delta Lake. You'll examine Spark optimization techniques, such as partitioning, caching, and query tuning, and learn performance monitoring, troubleshooting, and best practices for efficient data engineering and analytics to address real-world challenges.

Languages Available: English | 日本語 | 한국어

Machine Learning Practitioner

Machine Learning Model Development

This comprehensive course provides a practical guide to developing traditional machine learning models on Databricks, emphasizing hands-on demonstrations and workflows using popular ML libraries. Participants will explore key ML techniques, including regression and clustering, while leveraging Databricks’ powerful capabilities. The course covers MLflow integration for model tracking, Databricks Feature Store for feature management, and Optuna for hyperparameter tuning. Additionally, participants will learn how to accelerate model training with Databricks AutoML. By the end of the course, learners will have real-world, practical skills to develop, optimize, and deploy machine learning models efficiently in the Databricks environment.

Languages Available: English | 日本語 | Português BR | 한국어

AI/BI for Data Analysts

In this course, you’ll learn how to use the features Databricks provides for business intelligence needs: AI/BI Dashboards and AI/BI Genie. As a Databricks Data Analyst, you will be tasked with creating AI/BI Dashboards and AI/BI Genie Spaces within the platform, managing the access to these assets by stakeholders and necessary parties, and maintaining these assets as they are edited, refreshed, or decommissioned over the course of their lifespan. This course intends to instruct participants on how to design dashboards for business insights, share those with collaborators and stakeholders, and maintain those assets within the platform. Participants will also learn how to utilize AI/BI Genie Spaces to support self-service analytics through the creation and maintenance of these environments powered by the Databricks Data Intelligence Engine.