DP-750: Azure Databricks Data Engineer Associate Exam Prep

<div>Master Azure Databricks and confidently prepare for the DP-750: Azure Databricks Data Engineer Associate certification with a course designed for real-world impact.</div><div><br></div><div>This course goes beyond theory to help you build production-ready data engineering solutions using Azure Databricks and Apache Spark. Whether you are preparing for the certification or aiming to transition into a data engineering role, this course equips you with the exact skills required in modern data platforms.</div><div><br></div><div>You will start by understanding how to design and implement scalable data pipelines, followed by deep hands-on experience with Delta Lake, including ACID transactions, schema enforcement, schema evolution, and time travel. You will also learn how to implement both batch and streaming ingestion pipelines using Auto Loader and Structured Streaming.</div><div><br></div><div>The course covers data transformation using Spark DataFrames and SQL, along with implementing the Medallion Architecture (Bronze, Silver, Gold) to structure reliable and maintainable pipelines. You will also explore Unity Catalog for data governance, security, and access control—an essential component for enterprise-grade solutions.</div><div><br></div><div>To ensure optimal performance, you will learn key optimization techniques such as partitioning, Z-Ordering, caching, and query tuning, along with monitoring and troubleshooting Databricks workloads.</div><div><br></div><div>By the end of this course, you will be fully prepared to pass the DP-750 certification and have the practical skills to design, build, and optimize data engineering solutions using Azure Databricks in real-world environments.</div>

What you'll learn:

• Implement and manage data ingestion using batch and streaming in Azure Databricks
• Design and manage Delta Lake tables with ACID transactions, schema evolution, and time travel
• Transform and process data using Apache Spark (DataFrames, SQL) in Databricks
• Optimize and monitor data workloads using partitioning, Z-Ordering, caching, and performance tuning