Data Management and Governance with Unity Catalog

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.

In this course, you will explore the core features and functionalities of Databricks Express Setup, a streamlined way to get started with Databricks. The course is designed to help you quickly set up and navigate a serverless workspace while providing a comprehensive understanding of the credit-based trial system, including the $400 allowance. Divided into six modules, it starts with an introduction to Databricks Express Setup, followed by an exploration of its key features and benefits. You will learn to create and manage serverless workspaces, perform exploratory data analysis using Unity Catalog, and gain insights into collaboration through data sharing.

Additionally, you’ll be guided through trial management and upgrade options, ensuring you can effectively transition from trial to paid accounts. The course also includes an internal-only module on the evolution of trial credits and account activation methods. By the end of the course, you will have a solid foundation in using Databricks Express Setup for data and AI workloads, enabling you to confidently explore and analyze data, collaborate with peers, and manage your Databricks environment efficiently.

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 DLT and Workflows 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 Databricks Asset 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.