Implement data engineering solutions using Azure Databricks

Implement data engineering solutions using Azure Databricks (DP-750T00)

Delivery methods

On-Site, Virtual

Duration

4 days

Master end-to-end data engineering with Azure Databricks and Unity Catalog. This course moves from foundational setup to production deployment, covering environment configuration and enterprise-grade governance. Learn to build robust ingestion pipelines, implement security with Unity Catalog, and deploy optimized workloads. By the end, you will have the practical skills to implement, secure, and maintain scalable lakehouse solutions that meet rigorous enterprise requirements.

Learning Objectives

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

The target audience is data engineers who have fundamental knowledge of data analytics concepts, a basic understanding of cloud storage, and familiarity with data organization principles. They should be comfortable working with SQL and have experience using Python, including notebooks, for data engineering tasks. Learners are expected to have a good understanding of Azure Databricks workspaces and Unity Catalog, along with familiarity with data access patterns and core data engineering and data warehouse concepts. In addition, they should have foundational knowledge of Azure security, including Microsoft Entra ID, and be familiar with Git version control fundamentals.

Prerequisites

Fundamental knowledge of data analytics concepts
Basic understanding of cloud storage concepts
Familiarity with SQL and data organization principles
Good understanding of Azure Databricks workspaces and Unity Catalog concepts
Familiarity with SQL and data access patterns
Knowledge of Microsoft Entra ID and Azure security fundamentals
Knowledge of fundamental data engineering and data warehouse concepts

1Set up and configure an Azure Databricks environment

Explore Azure Databricks

Exercise - Explore Azure Databricks

Understand Azure Databricks Architecture

Understand Azure Databricks Integrations

Select and Configure Compute in Azure Databricks

Exercise - Select and Configure Compute

Create and Organize Objects in Unity Catalog

Exercise - Create and Organize Objects

2Secure and govern Unity Catalog objects in Azure Databricks

Secure Unity Catalog Objects

Exercise - Secure Unity Catalog Objects

Govern Unity Catalog Objects

Exercise - Govern Unity Catalog Objects

3Prepare and process data with Azure Databricks

Design and Implement Data Modeling with Azure Databricks

Exercise - Design and Implement Data Modeling

Ingest Data into Unity Catalog

Exercise - Ingest Data

Cleanse, Transform, and Load Data into Unity Catalog

Exercise - Cleanse, Transform, and Load Data

Implement and Manage Data Quality Constraints with Azure Databricks

Exercise - Implement Data Quality Constraints

4Deploy and maintain data pipelines and workloads with Azure Databricks

Design and Implement Data Pipelines with Azure Databricks

Exercise - Design and Implement Data Pipelines

Implement Lakeflow Jobs with Azure Databricks

Exercise - Implement Lakeflow Jobs

Implement Development Lifecycle Processes in Azure Databricks

Exercise - Implement Development Lifecycle Processes

Monitor, Troubleshoot, and Optimize Workloads in Azure Databricks

Exercise - Monitor, Troubleshoot, and Optimize Workloads

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