Mastering Azure Databricks for Data Engineers

Updated in May 2025. This course now features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. This advanced course on Azure Databricks will empower you with the skills to manage complex data workflows efficiently. With a focus on advanced features like Unity Catalog, Delta Tables, and Databricks Ingestion Tools, you will gain hands-on experience in managing large-scale data pipelines, ensuring data consistency, and implementing data governance across the Databricks platform. By the end of the course, you'll have a comprehensive understanding of Databricks' capabilities in data management, equipping you to handle enterprise-level data solutions. The course begins by introducing Unity Catalog, showing how it can be set up and used for managing user access and securing objects in your Databricks environment. You’ll learn how to configure the Unity Catalog and work with various securable objects, ensuring a secure and organized data landscape. As you progress, you will dive deeper into Delta Lake and Delta Tables, starting with an introduction to Delta Lake's features, followed by a thorough exploration of how to create and manage Delta Tables, including reading and optimizing them for performance. In the later modules, you’ll explore Databricks' incremental ingestion tools. You will be introduced to the architecture and use cases of incremental data ingestion, including how to leverage tools like Copy Into and Databricks Autoloader with schema evolution. You’ll also work with streaming data ingestion to ensure real-time data processing with minimal effort. The course concludes with an introduction to Delta Live Tables (DLT), where you’ll learn to create DLT pipelines and workloads using SQL and Python, solidifying your knowledge in streamlining real-time analytics. This course is ideal for experienced data engineers, data architects, and data scientists who want to specialize in Azure Databricks. Prior experience with cloud-based data platforms, SQL, and Python is recommended. With a focus on practical application, this course is designed to take your expertise in data management to the next level.

Updated in May 2025. This course now features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. In this course, you will learn how to automate and implement projects in Azure Databricks, empowering you to efficiently manage workflows, code repositories, and automation features. By the end of the course, you’ll gain practical experience in building robust data engineering solutions with Azure Databricks, making you proficient in project automation techniques within a cloud-based platform. The course begins with an introduction to Databricks' project and automation features, including managing code with Databricks Repos, creating workflows, and working with both the Databricks REST API and CLI. As you progress, you'll learn how to set up and manage your Databricks environment, storage solutions, and implement security and resource policies for a complete solution. You will also be guided through building and testing your code, with a focus on integrating data through bronze, silver, and gold layers. The capstone project allows you to apply your learning practically. You'll start with designing your project’s scope and proceed through implementing essential stages such as decoupling data ingestion, setting up a unified catalog, and integrating source control. As you design and implement various layers in your architecture, the project will lead you to integration testing and continuous integration/continuous deployment (CI/CD) pipeline development. This course is ideal for data engineers, cloud engineers, and developers who wish to enhance their skills in Databricks and automation. Some familiarity with cloud computing concepts and basic coding skills are recommended, and the difficulty level is intermediate, suitable for those with a foundational understanding of data engineering or Azure services.