Microsoft Azure Databricks Data Engineer Associate (DP-750) Cert Prep by Microsoft Press
2h 38mIntermediate2026-08-12
Authors
Microsoft Press
Microsoft
Course details
Master data engineering on Azure Databricks while preparing for the Microsoft Azure Databricks Data Engineer Associate (DP-750) certification exam. Learn how to set up and configure workspaces and compute resources, then apply data governance with Unity Catalog and optimize data design with Delta Lake. Explore batch and streaming ingestion using Spark Structured Streaming and Azure Event Hubs, and work through joins, data reshaping, schema enforcement, and validation to maintain high data quality. Learn how to assemble, schedule, and orchestrate production pipelines with LakeFlow jobs, and apply CI/CD practices using Git workflows and the testing pyramid. An ideal fit for data professionals looking to strengthen their Azure Databricks expertise, this course equips you with the skills you need to build efficient, reliable data engineering solutions for real-world applications.
Learning objectives
Configure Azure Databricks workspaces and compute resources for data engineering workloads.
Apply data governance with Unity Catalog and optimize data storage using Delta Lake.
Ingest data using batch and streaming methods with Spark Structured Streaming and Azure Event Hubs.
Transform data through joins, reshaping, schema enforcement, and quality validation.
Orchestrate production pipelines with LakeFlow jobs and CI/CD practices using Git workflows.
Learning objectives
Configure Azure Databricks workspaces and compute resources for data engineering workloads.
Apply data governance with Unity Catalog and optimize data storage using Delta Lake.
Ingest data using batch and streaming methods with Spark Structured Streaming and Azure Event Hubs.
Transform data through joins, reshaping, schema enforcement, and quality validation.
Orchestrate production pipelines with LakeFlow jobs and CI/CD practices using Git workflows.
Concepts
Introduction
- Course welcome
Foundations. Workspaces, Compute, and Notebooks
- Learning objectives
- Course introduction and the DP-750 landscape
- The lakehouse pattern and why Azure Databricks exists
- Provisioning and navigating the workspace
- Configuring compute for the job at hand
- Working with notebooks across languages
Unity Catalog. Structure, Security, and Governance
- Learning objectives
- The Unity Catalog object model
- Creating and organizing catalog objects
- Permissions and fine-grained access control
- Identity, secrets, and authentication patterns
- Lineage, audit, and data discovery
- Sharing data securely
Designing Data for the Lakehouse
- Learning objectives
- Delta Lake and table design
- Partitioning, clustering, and storage optimization
- Change-tracking patterns - SCD and temporal tables
- The medallion architecture
Ingesting and Transforming Data
- Learning objectives
- Batch ingestion patterns
- Streaming ingestion patterns
- Cleansing, profiling, and core transformations
- Joins, set operations, and reshaping
- Loading data - Merge, insert, and append
- Data quality enforcement
- Spark optimization fundamentals
Production Pipelines and Operations
- Learning objectives
- Pipeline design - Notebooks, declarative pipelines, and Lakeflow Jobs
- Orchestration - Triggers, schedules, and error handling
- Git workflow and the testing strategy
- Asset Bundles and deployment
- Monitoring, troubleshooting, and cost management
- Performance - Reading the Spark UI
Conclusion
- Exam prep - Next steps