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DP-750T00: Implement data engineering solutions using Azure Databricks
This intermediate course takes data engineers from setup to production with Azure Databricks. You’ll learn to configure Databricks environments and compute, apply governance and security with Unity Catalog, and design lakehouse data models. It also covers building batch and streaming ingestion pipelines, transforming and validating data, and deploying, monitoring and optimising workloads. It prepares you for the Microsoft Certified: Azure Databricks Data Engineer Associate (DP-750) exam.
Available in Instructor-Led or Virtual formats.
Duration
4 Days
Modules
15 Modules
Course type
Microsoft
Course Overview
The DP-750T00 course equips data engineers with the skills to design, build, secure, and manage enterprise-scale lakehouse solutions using Azure Databricks and Unity Catalog. It covers Databricks architecture, environment configuration, data governance, modeling, ingestion, transformation, quality management, pipeline development, automation, monitoring, and optimization. Participants learn to implement scalable data engineering workflows, apply security and compliance practices, and integrate Azure services to support analytics, AI, and business intelligence workloads.
Before You Begin
- Fundamentals of Data Engineering and Analytics
- Azure Databricks and Git Fundamentals
- SQL and Python Programming Skills
Key outcomes
- Learn Azure Databricks architecture, workspace components, compute configuration, integrations, and environment setup.
- Learn how to secure, organize, and govern data assets using permissions, access controls, policies, lineage, and auditing.
- Learn to design data models, ingest data, transform datasets, and implement data quality controls using Azure Databricks.
- Learn to design, automate, schedule, and manage scalable data pipelines and workflows using Azure Databricks and Lakeflow Jobs.
- Learn development lifecycle practices, CI/CD processes, troubleshooting techniques, workload monitoring, and performance optimization for enterprise data solutions.
Tools you’ll use
Azure Databricks
Unity Catalog
Lakeflow Jobs
SQL
Python
Git and Version Control Practices
Azure Service Integrations
Azure Databricks Compute and Cluster Management
Skills you’ll gain
- Azure Databricks Environment Management
- Data Governance with Unity Catalog
- Data Modeling and Processing
- Data Ingestion and Transformation
- Data Quality Management
- Data Pipeline Development
- DevOps and Lifecycle Practices
- Workload Monitoring and Optimization
What You’ll Learn
15 modules
1Module 1: Explore Azure Databricks
1.1 Introduction
1.2 Get started with Azure Databricks
1.3 Identify Azure Databricks workloads
1.4 Understand key concepts
1.5 Data governance using Unity Catalog and Microsoft Purview
1.6 Exercise – Explore Azure Databricks
1.7 Summary
1.2 Get started with Azure Databricks
1.3 Identify Azure Databricks workloads
1.4 Understand key concepts
1.5 Data governance using Unity Catalog and Microsoft Purview
1.6 Exercise – Explore Azure Databricks
1.7 Summary
2Module 2: Understand Azure Databricks architecture
2.1 Introduction
2.2 Understand Azure Databricks architecture
2.3 Understand Unity Catalog managed storage
2.4 Understand external storage
2.5 Understand default storage
2.6 Summary
2.2 Understand Azure Databricks architecture
2.3 Understand Unity Catalog managed storage
2.4 Understand external storage
2.5 Understand default storage
2.6 Summary
3Module 3: Understand Azure Databricks Integrations
3.1 Introduction
3.2 Understand integration with Microsoft Fabric
3.3 Understand integration with Power BI
3.4 Understand integration with VS Code
3.5 Understand integration with Power Platform
3.6 Understand integration with Copilot Studio
3.7 Understand integration with Microsoft Purview
3.8 Understand integration with Microsoft Foundry
3.9 Summary
3.2 Understand integration with Microsoft Fabric
3.3 Understand integration with Power BI
3.4 Understand integration with VS Code
3.5 Understand integration with Power Platform
3.6 Understand integration with Copilot Studio
3.7 Understand integration with Microsoft Purview
3.8 Understand integration with Microsoft Foundry
3.9 Summary
4Module 4: Select and Configure Compute in Azure Databricks
4.1 Introduction
4.2 Choose an appropriate compute type
4.3 Configure compute performance
4.4 Configure compute features
4.5 Install libraries for compute
4.6 Configure compute access
4.7 Exercise – Select and Configure Compute in Azure Databricks
4.8 Summary
4.2 Choose an appropriate compute type
4.3 Configure compute performance
4.4 Configure compute features
4.5 Install libraries for compute
4.6 Configure compute access
4.7 Exercise – Select and Configure Compute in Azure Databricks
4.8 Summary
5Module 5: Create and organize objects in Unity Catalog
5.1 Introduction
5.2 Apply naming conventions
5.3 Create catalog
5.4 Create schema
5.5 Create tables and views
5.6 Create volumes
5.7 Implement DDL operations
5.8 Implement foreign catalog
55.9 Configure AI/BI Genie instructions
5.10 Exercise – Create and Organize Objects in Unity Catalog
5.11 Summary
5.2 Apply naming conventions
5.3 Create catalog
5.4 Create schema
5.5 Create tables and views
5.6 Create volumes
5.7 Implement DDL operations
5.8 Implement foreign catalog
55.9 Configure AI/BI Genie instructions
5.10 Exercise – Create and Organize Objects in Unity Catalog
5.11 Summary
6Module 6: Secure Unity Catalog objects
6.1 Introduction
6.2 Understand query lifecycle
6.3 Implement access control strategies
6.4 Understand fine-grained access control
6.5 Implement row filtering and column masking
6.6 Access Azure Key Vault secrets
6.7 Authenticate data access with service principals
6.8 Authenticate resource access with managed identities
6.9 Exercise – Secure Unity Catalog Objects
6.10 Summary
6.2 Understand query lifecycle
6.3 Implement access control strategies
6.4 Understand fine-grained access control
6.5 Implement row filtering and column masking
6.6 Access Azure Key Vault secrets
6.7 Authenticate data access with service principals
6.8 Authenticate resource access with managed identities
6.9 Exercise – Secure Unity Catalog Objects
6.10 Summary
7Module 7: Govern Unity Catalog objects
7.1 Introduction
7.2 Create and preserve table definitions
7.3 Configure ABAC with tags and policies
7.4 Apply data retention policies
7.5 Set up and manage data lineage
7.6 Configure audit logging
7.7 Design secure Delta Sharing strategy
7.8 Exercise – Govern Unity Catalog Objects
7.9 Summary
7.2 Create and preserve table definitions
7.3 Configure ABAC with tags and policies
7.4 Apply data retention policies
7.5 Set up and manage data lineage
7.6 Configure audit logging
7.7 Design secure Delta Sharing strategy
7.8 Exercise – Govern Unity Catalog Objects
7.9 Summary
8Module 8: Design and implement data modeling with Azure Databricks
8.1 Introduction
8.2 Design ingestion logic and data source configuration
8.3 Choose a data ingestion tool
8.4 Choose a data table format
8.5 Design and implement a data partitioning scheme
8.6 Choose a slowly changing dimension (SCD) type
8.7 Implement a slowly changing dimension (SCD) type
8.8 Design and implement a temporal (history) table to record changes over time
8.9 Choose granularity on a column or table based on requirements
8.10 Choose managed vs external tables
8.11 Design and implement a clustering strategy
8.12 Exercise – Design and Implement Data Modeling with Azure Databricks
8.13 Summary
8.2 Design ingestion logic and data source configuration
8.3 Choose a data ingestion tool
8.4 Choose a data table format
8.5 Design and implement a data partitioning scheme
8.6 Choose a slowly changing dimension (SCD) type
8.7 Implement a slowly changing dimension (SCD) type
8.8 Design and implement a temporal (history) table to record changes over time
8.9 Choose granularity on a column or table based on requirements
8.10 Choose managed vs external tables
8.11 Design and implement a clustering strategy
8.12 Exercise – Design and Implement Data Modeling with Azure Databricks
8.13 Summary
9Module 9: Ingest data into Unity Catalog
9.1 Introduction
9.2 Ingest data with Lakeflow Connect
9.3 Ingest data with notebooks
9.4 Ingest data with SQL methods
9.5 Ingest data with CDC feed
9.6 Ingest data with Spark Structured Streaming
9.7 Ingest data with Auto Loader
9.8 Ingest data with Lakeflow Spark Declarative Pipelines
9.9 Exercise – Ingest Data into Unity Catalog
9.10 Summary
9.2 Ingest data with Lakeflow Connect
9.3 Ingest data with notebooks
9.4 Ingest data with SQL methods
9.5 Ingest data with CDC feed
9.6 Ingest data with Spark Structured Streaming
9.7 Ingest data with Auto Loader
9.8 Ingest data with Lakeflow Spark Declarative Pipelines
9.9 Exercise – Ingest Data into Unity Catalog
9.10 Summary
10Module 10: Cleanse, transform, and load data into Unity Catalog
10.1 Introduction
10.2 Profile data
10.3 Choose column data types
10.4 Resolve duplicates and nulls
10.5 Transform data with filters and aggregations
10.6 Transform data with joins and set operators
10.7 Transform data with denormalization and pivots
10.8 Load data with merge, insert, and append
10.9 Exercise – Cleanse, Transform, and Load Data into Unity Catalog
10.10 Summary
10.2 Profile data
10.3 Choose column data types
10.4 Resolve duplicates and nulls
10.5 Transform data with filters and aggregations
10.6 Transform data with joins and set operators
10.7 Transform data with denormalization and pivots
10.8 Load data with merge, insert, and append
10.9 Exercise – Cleanse, Transform, and Load Data into Unity Catalog
10.10 Summary
11Module 11: Implement and manage data quality constraints with Azure Databricks
11.1 Introduction
11.2 Implement validation checks
11.3 Implement data type checks
11.4 Detect and manage schema drift
11.5 Manage data quality with pipeline expectations
11.6 Exercise – Implement and Manage Data Quality Constraints with Azure Databricks
11.7 Summary
11.2 Implement validation checks
11.3 Implement data type checks
11.4 Detect and manage schema drift
11.5 Manage data quality with pipeline expectations
11.6 Exercise – Implement and Manage Data Quality Constraints with Azure Databricks
11.7 Summary
12Module 12: Design and implement data pipelines with Azure Databricks
12.1 Introduction
12.2 Design order of operations for a pipeline
12.3 Choose notebook vs Lakeflow Pipelines
12.4 Design Lakeflow job logic
12.5 Design error handling in pipelines and jobs
12.6 Create pipeline with notebook
12.7 Create pipeline with Lakeflow Spark Declarative Pipelines
12.8 Exercise – Design and Implement Data Pipelines with Azure Databricks
12.9 Summary
12.2 Design order of operations for a pipeline
12.3 Choose notebook vs Lakeflow Pipelines
12.4 Design Lakeflow job logic
12.5 Design error handling in pipelines and jobs
12.6 Create pipeline with notebook
12.7 Create pipeline with Lakeflow Spark Declarative Pipelines
12.8 Exercise – Design and Implement Data Pipelines with Azure Databricks
12.9 Summary
13Module 13: Implement Lakeflow Jobs with Azure Databricks
13.1 Introduction
13.2 Create job setup and configuration
13.3 Configure job triggers
13.4 Schedule a job
13.5 Configure job alerts
13.6 Configure automatic restarts
13.7 Exercise – Implement Lakeflow Jobs with Azure Databricks
13.8 Summary
13.2 Create job setup and configuration
13.3 Configure job triggers
13.4 Schedule a job
13.5 Configure job alerts
13.6 Configure automatic restarts
13.7 Exercise – Implement Lakeflow Jobs with Azure Databricks
13.8 Summary
14Module 14: Implement development lifecycle processes in Azure Databricks
14.1 Introduction
14.2 Apply Git version control best practices
14.3 Manage branching and pull requests
14.4 Implement testing strategy
14.5 Configure and package Declarative Automation Bundles
14.6 Deploy bundle with Databricks CLI
14.7 Exercise – Implement Development Lifecycle Processes in Azure Databricks
Summary
14.2 Apply Git version control best practices
14.3 Manage branching and pull requests
14.4 Implement testing strategy
14.5 Configure and package Declarative Automation Bundles
14.6 Deploy bundle with Databricks CLI
14.7 Exercise – Implement Development Lifecycle Processes in Azure Databricks
Summary
15Module 15: Monitor, troubleshoot and optimize workloads in Azure Databricks
15.1 Introduction
15.2 Monitor and manage cluster consumption
15.3 Troubleshoot and repair Lakeflow Jobs
15.4 Troubleshoot Spark jobs and notebooks
15.5 Investigate caching, skewing, spilling, shuffle
15.6 Implement log streaming with Azure Log Analytics
15.7 Exercise – Monitor, Troubleshoot and Optimize Workloads in Azure Databricks
15.8 Summary
15.2 Monitor and manage cluster consumption
15.3 Troubleshoot and repair Lakeflow Jobs
15.4 Troubleshoot Spark jobs and notebooks
15.5 Investigate caching, skewing, spilling, shuffle
15.6 Implement log streaming with Azure Log Analytics
15.7 Exercise – Monitor, Troubleshoot and Optimize Workloads in Azure Databricks
15.8 Summary
Certification & Credentials
Certificate track
4 Days
High-Quality Video Lessons
Digital courseware, e-books & audio access
Recorded sessions, accessible on tablet & phone
AI mentor support & module quizzes
Learning Resources
Videos & Engaging Visual Content
Podcasts & Audiobooks
Expert discussions and audio episodes covering key topics
E-Books
Downloadable guides
Module-Wise Quizzes
Interactive quizzes after each module to reinforce learning.
Additional Resources
Study materials, exam prep guides, sandbox labs, and community tutorials.
Frequently asked questions
What is the DP-750T00 course, and who is it designed for?
The DP-750T00 course is designed for data engineers who want to build secure, scalable lakehouse solutions using Azure Databricks and Unity Catalog. It covers configuring Databricks environments, managing compute, governing data assets, and developing production pipelines that support analytics, AI, and business intelligence workloads.
What knowledge or skills are required before taking this course?
Learners should understand data engineering and analytics fundamentals, including ETL/ELT processes, data warehousing, data modeling, and data access patterns. Basic SQL and Python programming skills are required, along with familiarity with Azure Databricks workspaces, Unity Catalog concepts, cloud storage, and basic Azure security practices. The curriculum also lists Git fundamentals among the prerequisites.
What will I learn about data security and governance?
You will learn to create and organize Unity Catalog objects and protect catalogs, schemas, tables, views, and volumes through permissions, access controls, and ownership. The course also covers data governance practices such as lineage, auditing, metadata management, policies, classifications, and compliance controls.
Does the course cover data ingestion, transformation, and data quality?
Yes. The course covers designing scalable data models, ingesting structured and unstructured data into Unity Catalog, and cleansing, transforming, validating, and loading data through reliable pipelines. You will also learn to apply data quality rules, constraints, validations, and monitoring practices to maintain accurate, consistent, and trusted datasets.
Will I learn how to deploy and maintain production data pipelines?
Yes. You will learn to design, build, automate, and manage scalable data pipelines, including configuring and scheduling Lakeflow Jobs. The course also covers version control, testing, deployment, collaboration, and continuous integration and continuous delivery (CI/CD) practices, alongside monitoring job performance, troubleshooting failures, managing resources, and optimizing workloads.
| Duration | 4 Days |
|---|---|
| Modules | 15 Modules |
| Course type | Microsoft |
| Category | Microsoft |
| Prerequisites | Fundamentals of Data Engineering and Analytics, Azure Databricks and Git Fundamentals, SQL and Python Programming Skills |
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