NFO
Mimir, Keeper of the Well of Wisdom
DP-750: Azure Databricks Data Engineer Associate Exam Prep
https://www.udemy.com/course/azure-databricks-data-engineering/
Year : 2026
Language : English
Level : Intermediate Level
Category : IT & Software
Subcategory : IT Certifications
Duration : 12h 33m
Lectures : 76
Rating : 4.7/5 (56 reviews)
Students : 690
INSTRUCTOR(S)
HEADLINE
Prepare for the DP-750 Exam with Instructor led hands-on labs
and videos
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
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)
WHO IS THIS COURSE 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
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.
COURSE CONTENT
Chapter 1: Introduction
1. Introduction
Chapter 2: Databricks Foundations
2. Introduction to Azure Databricks
3. Azure and Databricks Integration Architecture
4. Lab: Deploying a Databricks Workspace (Hands-On Lab)
5. Understanding Data Warehouse, Data Lake and Data
Lakehouse
6. Understanding Spark: Introduction and Evolution from
Hadoop
7. Understanding Spark: Deep Dive and Usage
8. Lab: Deploying a Managed Compute Instance (Hands-On Lab)
9. Architectural Decision: Choosing an Appropriate Compute
Target
10. Lab: Setting Up the GitHub Repo for the Labs (Hands-On
Lab)
Chapter 3: Data Analytics using Apache Spark, SQL and Unity
Catalog
11. Unity Catalog: Schemas, Tables and Volumes
12. Lab: Getting Comfortable with Spark (Hands-On Lab)
13. Lab: Advanced Apache Spark Operations (Hands-On Lab)
14. Lab: Delta Table Operations (Hands-On Lab)
15. Introduction to the Medallion Architecture
16. Lab: Implementing the Medallion Architecture with Spark
(Hands-On Lab)
17. Views: Materialized v/s non-materialized
18. Lab: Creating Materialized and Non-Materialized Views
(Hands-On Lab)
19. Introduction to AI/BI Genie for Data Analysis
20. Lab: Working with AI/BI Genie (Hands-On Lab)
Chapter 4: Secure and Govern Unity Catalog Objects
21. Introduction to Managed Identity Auth for Databricks
22. Lab: Creating an External Connection for ADLS (Hands-On
Lab)
23. Lab: Creating a New Unity Catalog on External Location
ADLS (Hands-On Lab)
24. Understanding Azure Key Vault
25. Lab: Key Vault integration with Azure Databricks (Hands-
On Lab)
26. Understanding Table, Column and row-level access and
security
27. Lab: Row-level and Column-level Security for Tables
(Hands-On Lab)
28. Lab: Row-level and Column-level Security for Views
(Hands-On Lab)
Chapter 5: Design and Implement Data Modelling
29. Choose a Data Table Format: Parquet, Delta, CSV, JSON,
Icerberg etc
30. Managed v/s External Tables
31. Lab: Working with Managed and External Tables (Hands-On
Lab)
32. Design Data Partitioning Scheme
33. Lab: Performance Tuning with Data Partitioning (Hands-On
Lab)
34. Understanding OPTIMIZE Command
35. Understanding the VACUUM Command
36. Lab: Performance Tuning with OPTIMIZE and VACUUM (Hands-
On Lab)
37. Understand Z-Ordering and Liquid Clustering
38. Lab: Performance Tuning with Z-Ordering and Liquid
Clustering (Hands-On Lab)
39. Understanding the Star-Schema Model for Data Modelling
40. Introduction to SCD: Type 1, Type 2 and Type 3
41. Introduction to our mini "FCA Financial Reporting"
project
42. Lab: Implementing SCD Type 2 (Hands-On Lab)
43. Lab: Implementing Change Data Feed and Audit Trail
(Hands-On Lab)
Chapter 6: Ingest Data into Unity Catalog
44. The Databricks Data Ingestion Landscape
45. Introduction to Ingesting Data with Lakeflow Connect
46. Lab: Ingest Data using Connectors (Hands-On Lab)
47. Introduction to Data Ingestion with Notebooks
48. Lab: Ingest Data with Notebooks (Hands-On Lab)
49. Introduction to Data Ingestion with CTAS Query
50. Lab: Ingest Data with CTAS (Hands-On Lab)
51. Introduction to Data Ingestion with COPY INTO Query
52. Lab: Ingest data with "COPY INTO" (Hands-On Lab)
53. Introduction to Spark Structured Streaming
54. Lab: Ingest Data with Spark Structured Streaming (Hands-
On Lab)
55. Introduction to Data Ingestion with Auto Loader
56. Auto Loader v/s COPY INTO
57. Lab: Ingest Data with Auto Loader (Hands-On Lab)
58. Apply a Data Ingestion Decision Framework
Chapter 7: Cleanse, Transform and Load Data into Unity Catalog
59. Introduction to Data Profiling and Statistical Insights
60. Lab: Profile Data to Generate Statistical Insights
(Hands-On Lab)
61. Understanding Data Types, Null Handling and
Deduplication
62. Lab: Data Types, Null Handling and Deduplication (Hands-
On Lab)
63. Lab: Aggregates, Filters and Grouping Mechanisms (Hands-
On Lab)
64. Introduction to JOINS
65. Lab: Working with JOINS (Hands-On Lab)
66. Understanding Normalization and Table Data Pivoting
67. Lab: Pivot and Un-Pivoting Data (Hands-On Lab)
Chapter 8: Design and Implement Data Pipelines
68. Introduction to Data Pipelines with Notebooks and SDPs
69. Lab: Create a Pipeline using Notebooks (Hands-On Lab)
70. Lab: add pipeline parameters (Hands-On Lab)
71. Lab: Create a Pipeline using the SDP approach (Hands-On
Lab)
Chapter 9: Apply Version Control and Development Lifecycle
Processes
72. Introduction to Git for Azure Databricks
73. Some Git and GitHub Jargons
74. Lab: Raising PRs and managing conflicts (Hands-On Lab)
75. Introduction to Declarative Automation Bundles (DABs)
76. Lab: Setup a DAB in your workspace (Hands-On Lab)
DATES
Published : 2026-04-04
Last Updated : 2026-05-08
If you fear the truth, dont come to my well.
CRC32: 34820697ece43b4d6c219e72d71cb6ca96fdc53d