Implement data engineering solutions using Azure Databricks (DP-750T00)

Master end-to-end data engineering with Azure Databricks and Unity Catalog. This course moves from foundational setup to production deployment, covering environment configuration and enterprise-grade governance. Learn to build robust ingestion pipelines, implement security with Unity Catalog, and deploy optimized workloads. By the end, you will have the practical skills to implement, secure, and maintain scalable lakehouse solutions that meet rigorous enterprise requirements.

Audience

The target audience is data engineers who have fundamental knowledge of data analytics concepts, a basic understanding of cloud storage, and familiarity with data organization principles. They should be comfortable working with SQL and have experience using Python, including notebooks, for data engineering tasks. Learners are expected to have a good understanding of Azure Databricks workspaces and Unity Catalog, along with familiarity with data access patterns and core data engineering and data warehouse concepts. In addition, they should have foundational knowledge of Azure security, including Microsoft Entra ID, and be familiar with Git version control fundamentals.

Course Outline

Set up and configure an Azure Databricks environment
Explore Azure Databricks
Understand Azure Databricks architecture
Understand Azure Databricks Integrations
Select and Configure Compute in Azure Databricks
Create and organize objects in Unity Catalog
Secure and Govern Unity Catalog Objects in Azure Databricks
Secure Unity Catalog objects
Govern Unity Catalog objects
Prepare and process data with Azure Databricks
Design and implement data modeling with Azure Databricks
Ingest data into Unity Catalog
Cleanse, transform, and load data into Unity Catalog
Implement and manage data quality constraints with Azure Databricks
Deploy and maintain data pipelines and workloads with Azure Databricks
Design and implement data pipelines with Azure Databricks
Implement Lakeflow Jobs with Azure Databricks
Implement development lifecycle processes in Azure Databricks
Monitor, troubleshoot and optimize workloads in Azure Databricks