SQL Programming and Procedural Logic

In this course, you will explore core SQL programming and procedural logic skills within Databricks, focusing on how to build modular, maintainable, and production-ready analytics workflows. You’ll start by learning about key SQL constructs—such as Common Table Expressions, temporary views, and User Defined Functions—that make your SQL development cleaner and more reusable. The course then guides you through advanced SQL scripting techniques using compound statements, variables, control flow, conditionals, loops, and robust error handling to create structured and flexible SQL processes. Finally, you’ll discover how to orchestrate and modernize complex workflows with Lakeflow Jobs and migration strategies, transforming legacy tasks into modular, automated pipelines that leverage the full capabilities of the Databricks platform.

Note: Databricks Academy is transitioning from video lectures to a more streamlined PDF format with slides and notes for all self-paced courses. Please note that demo videos will still be available in their original format. We would love to hear your thoughts on this change, so please share your feedback through the course survey at the end. Thank you for being a part of our learning community!

Skill Level

Associate

Duration

Prerequisites

The content was developed for participants with these skills/knowledge/abilities:

⇾ A fundamental understanding of SQL workspaces, data warehouses, and Databricks notebooks (SQL, Python, and markdown)

⇾ Experience working with the Databricks Lakehouse architecture, including Delta Lake and Unity Catalog for data management and governance

⇾ Familiarity with creating and managing workflows for data pipelines and orchestration

⇾ Prior experience querying and analyzing data with SQL in relational databases, including use of stored procedures and dynamic or parameterized queries

⇾ A working knowledge of data migration or modernization projects, covering core data warehousing concepts such as ETL, schema design, data modeling, and BI Reporting

⇾ Awareness of data pipeline orchestration and compliance best practices, and understanding of ANSI SQL standards

Self-Paced

Custom-fit learning paths for data, analytics, and AI roles and career paths through on-demand videos

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Self-Paced

Custom-fit learning paths for data, analytics, and AI roles and career paths through on-demand videos

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Public and private courses taught by expert instructors across half-day to two-day courses

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Skills@Scale

Comprehensive training offering for large scale customers that includes learning elements for every style of learning. Inquire with your account executive for details

Upcoming Public Classes

Databricks One for Business Users

This brief 30-minute training introduces the audience to the features and capabilities of Databricks One.

DevOps Essentials for Data Engineering

This course explores software engineering best practices and DevOps principles, specifically designed for data engineers working with Databricks. Participants will build a strong foundation in key topics such as code quality, version control, documentation, and testing. The course emphasizes DevOps, covering core components, benefits, and the role of continuous integration and delivery (CI/CD) in optimizing data engineering workflows.

You will learn how to apply modularity principles in PySpark to create reusable components and structure code efficiently. Hands-on experience includes designing and implementing unit tests for PySpark functions using the pytest framework, followed by integration testing for Databricks data pipelines with Spark Declarative Pipeline and Jobs to ensure reliability.

The course also covers essential Git operations within Databricks, including using Databricks Git Folders to integrate continuous integration practices. Finally, you will take a high level look at various deployment methods for Databricks assets, such as REST API, CLI, SDK, and Declarative Automation Bundles (DABs), providing you with the knowledge of techniques to deploy and manage your pipelines.

By the end of the course, you will be proficient in software engineering and DevOps best practices, enabling you to build scalable, maintainable, and efficient data engineering solutions.

Note: This is the fourth course in the 'Data Engineering with Databricks' series.

Note: Databricks Academy is transitioning from video lectures to a more streamlined PDF format with slides and notes for all self-paced courses. Please note that demo videos will still be available in their original format. We would love to hear your thoughts on this change, so please share your feedback through the course survey at the end. Thank you for being a part of our learning community!

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

Free

Associate

Data Analyst

Databricks AI/BI for Self-Service Analytics

This course teaches you how to find answers in your organization's data using Databricks One, dashboards, and Genie spaces. You'll learn to explore interactive dashboards, ask plain-language questions with the conversational AI tool Genie, and share what you discover with your team securely. By the end, you'll have practical skills and a simple framework for getting clear, reliable insights from the data tools your team has already built for you.

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

Free

Onboarding