- Published 7/31/2026
- 1st Edition
- Online video 978-0-13-597486-5
Pass the DP750 and master data engineering with hands-on Azure Databricks projects.
The DP-750 exam validates the modern Azure Databricks toolkit: Lakeflow Spark Declarative Pipelines for low-code pipeline authoring, Lakeflow Connect for managed ingestion, Lakeflow Jobs for orchestration, Unity Catalog for fine-grained access control and lineage, AI/BI Genie for natural-language data discovery, Databricks Asset Bundles for CI/CD, and Photon for query acceleration. Newer features the exam emphasizes--liquid clustering, attribute-based access control, deletion vectors, and structured streaming with Auto Loader--reflect where production data engineering is heading. This certification matters because organizations are consolidating fragmented data stacks onto governed lakehouses, and Microsoft is signaling that Azure Databricks fluency is now a distinct, certifiable specialty alongside Fabric and Synapse skills.
This course covers all four skill domains measured on the exam in five lessons of approximately 60 to 75 minutes each. Each lesson is organized around a coherent learning chunk--workspace foundations, Unity Catalog governance, lakehouse data design, ingestion and transformation, production operations--rather than as a feature inventory. Concept-first framing on slides establishes the mental models for the exam tests, and hands-on demonstrations in the Azure Databricks workspace reinforce those concepts in working code. Every named skill in the official Microsoft Learn study guide is addressed; the coverage map is documented in a companion internal reference.
Learners finish the course with the conceptual grounding to reason through scenario-based exam questions and the platform of fluency to execute on the job.
The course is structured around five learning chunks rather than as a feature inventory. The Foundations: Workspaces, Compute, and Notebooks lesson establishes platform foundations. The Unity Catalog: Structure, Security, and Governance lesson covers Unity Catalog as the governance fabric every later lesson assumes. The Designing Data for the Lakehouse lesson develops the data design principles that shape every pipeline. The Ingesting and Transforming Data lesson is the largest lesson, covering ingestion, transformation, and quality--matching the largest exam domain. The Production Pipelines and Operations lesson closes the loop with production operations: orchestration, deployment, monitoring, and optimization. Demonstrations build cumulatively across lessons, so by the final lesson the learner is troubleshooting a pipeline they themselves built earlier.
Skill Level:
Learn How To:
- Provision and configure an Azure Databricks workspace, choose appropriate compute for the task at hand, and work fluently in notebooks across SQL and Python
- Design and build the Unity Catalog object model--catalogs, schemas, volumes, tables, views, materialized views, foreign catalogs--with naming and isolation patterns that survive contact with production
- Secure and govern data using the full Unity Catalog toolkit: privilege grants, row filters, column masks, attribute-based access control, service principals, managed identities, Key Vault-backed secrets, lineage tracking, audit logs, retention policies, and Delta Sharing
- Reason about lakehouse data design--Delta Lake fundamentals, file formats, partitioning, liquid clustering, slowly changing dimensions, temporal tables, and the medallion architecture--as the conceptual backbone of every pipeline
- Ingest data through every supported path: Lakeflow Connect, notebook-based ingestion, SQL methods, change data capture, Spark Structured Streaming, Azure Event Hubs, and Auto Loader
- Cleanse, profile, and transform data using the full transformation toolkit, then enforce quality with validation checks, schema management, and pipeline expectations
- Build and ship production pipelines using Lakeflow Spark Declarative Pipelines and Lakeflow Jobs, with proper Git workflow, a complete testing strategy, and Asset Bundles for deployment via CLI or REST API
- Monitor, troubleshoot, and optimize workloads using the Spark UI, DAG analysis, OPTIMIZE/VACUUM, and Azure Monitor with Log Analytics
- Approach the DP-750 exam with the conceptual reasoning skills its scenario-based question format demands
Course requirement:
Pre-requisites:
- Working SQL skills (joins, aggregations, window functions)
- Basic Python (functions, dictionaries, list comprehensions)
- Familiarity with cloud computing concepts (storage, compute, identity)
- Basic Git usage (clone, commit, branch)
- Helpful but not required: prior exposure to Apache Spark, an Azure subscription for hands-on practice, prior experience with another DP-series exam
Who Should Take This Course:
Job titles:
- Azure Data Engineer
- Data Engineer (Azure Databricks)
- Senior Azure / Cloud Data Engineer
- Data Platform Engineer (Azure-focused)
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Table of Contents
Introduction
Foundations: Workspaces, Compute, and Notebooks
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
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
Delta Lake and table design
Partitioning, clustering, and storage optimization
Change-tracking patterns: SCD and temporal tables
The medallion architecture
Ingesting and Transforming Data
Batch ingestion patterns
Streaming ingestion patterns
Cleansing, profiling, and core transformations
Joins, set operations, and reshaping
Loading data: merge, insert, append
Data quality enforcement
Spark optimization fundamentals
Production Pipelines and Operations
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
Summary
