Automated Deployment with Declarative Automation Bundles

In this course, you will learn how to implement data governance at scale on Databricks using Unity Catalog, with a focus on attribute-based access control, observability, and federated sharing. You will configure ABAC with governed tags, migrate from legacy fine-grained controls, enable and use system tables for audit and cost monitoring, deploy Lakehouse Monitoring for data and model quality, interpret lineage for impact and compliance, and apply federated governance and Delta Sharing patterns for secure cross-cloud collaboration.

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.

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.

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

In this course, you'll learn how to effectively use Databricks for data analytics, with a specific focus on Databricks SQL. As a Databricks Data Analyst, your responsibilities will include finding relevant data, analyzing it for potential applications, and transforming it into formats that provide valuable business insights. 

You will also understand your role in managing data objects and how to manipulate them within the Databricks Data Intelligence Platform, using tools such as Notebooks, the SQL Editor, and Databricks SQL. 

Additionally, you will learn about the importance of Unity Catalog in managing data assets and the overall platform. Finally, the course will provide an overview of how Databricks facilitates performance optimization and teach you how to access Query Insights to understand the processes occurring behind the scenes when executing SQL analytics on Databricks.

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