DP-750: Azure Databricks Data Engineer Associate Exam Prep

Name: DP-750: Azure Databricks Data Engineer Associate Exam Prep
Rating: 4.6 (72 reviews)

Prepare for the DP-750 Exam with Instructor led hands-on labs and videos

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Created byKuljot Singh Bakshi

Last updated 5/2026

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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

Course content

9 sections • 76 lectures • 12h 33m total length

Introduction2:44

Introduction to Azure Databricks13:12
Azure and Databricks Integration Architecture4:51
Lab: Deploying a Databricks Workspace (Hands-On Lab)7:27
Understanding Data Warehouse, Data Lake and Data Lakehouse8:56
Understanding Spark: Introduction and Evolution from Hadoop10:51
Understanding Spark: Deep Dive and Usage14:54
Lab: Deploying a Managed Compute Instance (Hands-On Lab)5:26
Architectural Decision: Choosing an Appropriate Compute Target9:41
Lab: Setting Up the GitHub Repo for the Labs (Hands-On Lab)3:05

Unity Catalog: Schemas, Tables and Volumes14:11
Lab: Getting Comfortable with Spark (Hands-On Lab)19:22
Lab: Advanced Apache Spark Operations (Hands-On Lab)9:38
Lab: Delta Table Operations (Hands-On Lab)15:50
Introduction to the Medallion Architecture5:16
Lab: Implementing the Medallion Architecture with Spark (Hands-On Lab)14:49
Views: Materialized v/s non-materialized8:00
Lab: Creating Materialized and Non-Materialized Views (Hands-On Lab)18:19
Introduction to AI/BI Genie for Data Analysis4:58
Lab: Working with AI/BI Genie (Hands-On Lab)10:07

Introduction to Managed Identity Auth for Databricks5:32
Lab: Creating an External Connection for ADLS (Hands-On Lab)14:06
Lab: Creating a New Unity Catalog on External Location ADLS (Hands-On Lab)5:42
Understanding Azure Key Vault5:01
Lab: Key Vault integration with Azure Databricks (Hands-On Lab)15:03
Understanding Table, Column and row-level access and security8:30
Lab: Row-level and Column-level Security for Tables (Hands-On Lab)12:21
Lab: Row-level and Column-level Security for Views (Hands-On Lab)7:26

Choose a Data Table Format: Parquet, Delta, CSV, JSON, Icerberg etc13:50
Managed v/s External Tables8:28
Lab: Working with Managed and External Tables (Hands-On Lab)16:10
Design Data Partitioning Scheme10:44
Lab: Performance Tuning with Data Partitioning (Hands-On Lab)8:01
Understanding OPTIMIZE Command3:24
Understanding the VACUUM Command5:07
Lab: Performance Tuning with OPTIMIZE and VACUUM (Hands-On Lab)12:14
Understand Z-Ordering and Liquid Clustering8:27
Lab: Performance Tuning with Z-Ordering and Liquid Clustering (Hands-On Lab)11:27
Understanding the Star-Schema Model for Data Modelling8:35
Introduction to SCD: Type 1, Type 2 and Type 315:19
Introduction to our mini "FCA Financial Reporting" project6:25
Lab: Implementing SCD Type 2 (Hands-On Lab)25:05
Lab: Implementing Change Data Feed and Audit Trail (Hands-On Lab)14:39

The Databricks Data Ingestion Landscape7:47
Introduction to Ingesting Data with Lakeflow Connect4:47
Lab: Ingest Data using Connectors (Hands-On Lab)2:36
Introduction to Data Ingestion with Notebooks4:03
Lab: Ingest Data with Notebooks (Hands-On Lab)11:53
Introduction to Data Ingestion with CTAS Query5:19
Lab: Ingest Data with CTAS (Hands-On Lab)11:22
Introduction to Data Ingestion with COPY INTO Query6:06
Lab: Ingest data with "COPY INTO" (Hands-On Lab)12:05
Introduction to Spark Structured Streaming5:14
Lab: Ingest Data with Spark Structured Streaming (Hands-On Lab)11:50
Introduction to Data Ingestion with Auto Loader6:35
Auto Loader v/s COPY INTO4:04
Lab: Ingest Data with Auto Loader (Hands-On Lab)13:10
Apply a Data Ingestion Decision Framework3:59

Introduction to Data Profiling and Statistical Insights7:49
Lab: Profile Data to Generate Statistical Insights (Hands-On Lab)11:46
Understanding Data Types, Null Handling and Deduplication10:11
Lab: Data Types, Null Handling and Deduplication (Hands-On Lab)20:46
Lab: Aggregates, Filters and Grouping Mechanisms (Hands-On Lab)12:08
Introduction to JOINS10:33
Lab: Working with JOINS (Hands-On Lab)20:35
Understanding Normalization and Table Data Pivoting7:14
Lab: Pivot and Un-Pivoting Data (Hands-On Lab)6:11

Requirements

Basic understanding of data concepts (tables, files, databases)
Introductory knowledge of Python (recommended but not mandatory)
Familiarity with SQL for querying and data transformation
Basic awareness of cloud concepts (Azure fundamentals is helpful)

Description

Master Azure Databricks and confidently prepare for the DP-750: Azure Databricks Data Engineer Associate certification with a course designed for real-world impact.

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.

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.

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.

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.

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.

Who this course is for:

Data engineers preparing for the DP-750: Azure Databricks Data Engineer Associate certification
Developers and analysts looking to build scalable data pipelines using Azure Databricks
Professionals transitioning into data engineering roles with a focus on Spark and Delta Lake
Azure professionals seeking hands-on experience with Databricks, Unity Catalog, and data workflows

DP-750: Azure Databricks Data Engineer Associate Exam Prep

What you'll learn

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Course content

Introduction1 lecture • 3min

Databricks Foundations9 lectures • 1hr 18min

Data Analytics using Apache Spark, SQL and Unity Catalog10 lectures • 2hr 1min

Secure and Govern Unity Catalog Objects8 lectures • 1hr 14min

Design and Implement Data Modelling15 lectures • 2hr 48min

Ingest Data into Unity Catalog15 lectures • 1hr 51min

Cleanse, Transform and Load Data into Unity Catalog9 lectures • 1hr 47min

Design and Implement Data Pipelines4 lectures • 55min

Apply Version Control and Development Lifecycle Processes5 lectures • 38min

Requirements

Description

Who this course is for: