DP-750T00: Implement data engineering solutions using Azure Databricks Training

DP-750 covers end to end data engineering with Azure Databricks and Unity Catalog. You'll learn to design, build, secure, and optimize lakehouse solutions that work at enterprise scale. It's a practical course focused on the skills data engineers actually use on the job.

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Course Overview

This course teaches data engineers how to build and manage modern data platforms on Azure Databricks. You'll start with environment setup and architecture fundamentals, then move into implementing governance with Unity Catalog, building ingestion and transformation pipelines, and deploying production workloads. It follows the natural progression of how you'd build a data platform from scratch.

What you will learn

Configure and manage Azure Databricks environments and compute resources
Implement data governance and security using Unity Catalog
Design efficient data models for lakehouse architectures
Build scalable batch and streaming data ingestion pipelines
Transform, validate, and load data into analytics-ready formats
Deploy, monitor, and optimize production-grade data pipelines

Who Should Attend This Course?

Data Engineer
Cloud Engineer
Data Scientist

Prerequisites

Required

Basic understanding of data analytics concepts
Familiarity with SQL
Experience with Python (notebooks)
Understanding of data organization principles

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📘 DP-750T00: Implement data engineering solutions using Azure Databricks Outline

Explore Azure Databricks

Introduction
Get started with Azure Databricks
Identify Azure Databricks workloads
Understand key concepts
Data governance using Unity Catalog and Microsoft Purview
Exercise - Explore Azure Databricks

Understand Azure Databricks Architecture

Introduction
Understand Azure Databricks architecture
Understand Unity Catalog managed storage
Understand external storage
Understand default storage (serverless compute)

Understand Azure Databricks Integrations

Introduction
Integration with Microsoft Fabric
Integration with Power BI
Integration with Visual Studio Code
Integration with Power Platform
Integration with Copilot Studio
Integration with Microsoft Purview
Integration with Microsoft Foundry

Select and Configure Compute in Azure Databricks

Introduction
Choose appropriate compute type
Configure compute performance
Configure compute features
Install libraries
Configure compute access
Exercise - Select and Configure Compute

Create and Organize Objects in Unity Catalog

Introduction
Apply naming conventions
Create catalog
Create schema
Create tables and views
Create volumes
Implement DDL operations
Implement foreign catalog
Configure AI/BI Genie instructions
Exercise - Create and Organize Objects

Secure Unity Catalog Objects

Introduction
Understand query lifecycle
Implement access control strategies
Understand fine-grained access control
Implement row filtering and column masking
Access Azure Key Vault secrets
Authenticate data access with service principals
Authenticate resource access with managed identities
Exercise - Secure Unity Catalog Objects

Govern Unity Catalog Objects

Introduction
Create and preserve table definitions
Configure ABAC with tags and policies
Apply data retention policies
Set up and manage data lineage
Configure audit logging
Design secure Delta Sharing strategy
Exercise - Govern Unity Catalog Objects

Design and Implement Data Modeling with Azure Databricks

Introduction
Design ingestion logic and data source configuration
Choose a data ingestion tool
Choose a data table format
Design and implement a data partitioning scheme
Choose a slowly changing dimension (SCD) type
Implement a slowly changing dimension (SCD) type 2
Design and implement a temporal (history) table
Choose granularity based on requirements
Choose managed vs unmanaged tables
Design and implement a clustering strategy
Exercise - Design and Implement Data Modeling

Ingest Data into Unity Catalog

Introduction
Ingest data with Lakeflow Connect
Ingest data with notebooks
Ingest data with SQL methods
Ingest data with CDC feed
Ingest data with Spark Structured Streaming
Ingest data with Auto Loader
Ingest data with Lakeflow Spark Declarative Pipelines
Exercise - Ingest Data

Cleanse, Transform, and Load Data into Unity Catalog

Introduction
Profile data
Choose column data types
Resolve duplicates and nulls
Transform data with filters and aggregations
Transform data with joins and set operations
Transform data with denormalization and pivots
Load data with merge, insert, and append
Exercise - Cleanse, Transform, and Load Data

Implement and Manage Data Quality Constraints with Azure Databricks

Introduction
Implement validation checks
Implement data type checks
Detect and manage schema drift
Manage data quality with pipeline expectations
Exercise - Implement Data Quality Constraints

Design and Implement Data Pipelines with Azure Databricks

Introduction
Design order of operations for a pipeline
Choose notebook vs Lakeflow Pipelines
Design Lakeflow job logic
Design error handling in pipelines and jobs
Create pipeline with notebook
Create pipeline with Lakeflow Spark Declarative Pipelines
Exercise - Design and Implement Data Pipelines

Implement Lakeflow Jobs with Azure Databricks

Introduction
Create job setup and configuration
Configure job triggers
Schedule a job
Configure job alerts
Configure automatic restarts
Exercise - Implement Lakeflow Jobs

Implement Development Lifecycle Processes in Azure Databricks

Introduction
Apply Git version control best practices
Manage branching and pull requests
Implement testing strategy
Configure and package Databricks Asset Bundles (DABs)
Deploy bundle with Databricks CLI
Exercise - Implement Development Lifecycle Processes

Monitor, Troubleshoot, and Optimize Workloads in Azure Databricks

Introduction
Monitor and manage cluster consumption
Troubleshoot and repair Lakeflow Jobs
Troubleshoot Spark jobs and notebooks
Investigate caching, skewing, spilling, and shuffle
Implement log streaming with Azure Log Analytics
Exercise - Monitor, Troubleshoot, and Optimize Workloads

What You Get with Microtek Learning

Instructor-Led Excellence

✓ Certified Instructor-led Training
✓ Top Industry Trainers
✓ Official Student Handbooks

Measurable Learning Outcomes

✓ Pre- & Post-Training Assessments
✓ Practice Tests
✓ Exam-Oriented Curriculum

Real-World Skill Building

✓ Hands-on Activities & Scenarios
✓ Interactive Online Courses
✓ Peer Collaboration (Not in self-paced)

Full Support & Perks

✓ Exam Scheduling Support ^*
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✓ Support from Certified Experts
✓ Gov. & Private Pricing ^*

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