Implement Data Engineering Solutions Using Azure Databricks (DP-750)
Course 8776
4 DAY COURSE

This course teaches data professionals how to implement data engineering solutions using Microsoft Fabric. Participants learn how to ingest, transform, orchestrate, and manage enterprise data solutions using Fabric workloads including Data Factory, Data Engineering, Data Warehouse, Real-Time Analytics, and Lakehouse architectures.

Learners will gain hands-on experience building scalable analytics solutions, implementing medallion architectures, managing pipelines, and optimizing data workflows using Microsoft Fabric’s unified analytics platform.

The course focuses on practical implementation scenarios that support modern enterprise analytics and AI initiatives.

Implement Data Engineering Solutions Using Azure Databricks (DP-750) Benefits

• Course Benefits

  • Learn how to build AI agents using Azure AI Foundry
  • Gain practical experience integrating AI models and tools
  • Develop conversational AI and automation workflows
  • Implement prompt orchestration and agent behaviors
  • Understand responsible AI and governance practices
  • Build enterprise-ready intelligent applications
  • Gain hands-on experience with Microsoft official labs
  • Supports hybrid and remote attendance through AnyWare®

  Prerequisites

  • Experience developing software applications
  • Basic knowledge of Azure services
  • Familiarity with REST APIs and JSON
  • Understanding of AI and cloud concepts recommended

  Exam Information

  • Microsoft Certified: Azure Databricks Data Engineer Associate

Azure Databricks Training Outline

Learning Objectives

Explore Azure Databricks

• Describe Azure Databricks architecture
• Identify Azure Databricks workloads
• Navigate the Azure Databricks workspace
• Use notebooks in Azure Databricks
• Use clusters in Azure Databricks

Use Apache Spark in Azure Databricks

• Describe Apache Spark concepts
• Work with Spark DataFrames
• Query data using Spark SQL
• Transform data with PySpark
• Visualize data in notebooks

Configure Azure Databricks workspaces

• Configure Azure Databricks workspaces
• Manage clusters and compute resources
• Configure cluster policies
• Configure workspace settings
• Manage Azure Databricks access

Use Azure Databricks for data engineering workloads

• Ingest data into Azure Databricks
• Transform and clean data
• Work with Delta Lake tables
• Optimize data processing workloads
• Implement batch and streaming solutions

Use Delta Lake in Azure Databricks

• Describe Delta Lake capabilities
• Create Delta tables
• Manage table versions
• Use Delta Lake transactions
• Optimize Delta Lake performance

Build data pipelines with Azure Databricks

• Create Azure Databricks workflows
• Schedule jobs and pipelines
• Configure pipeline orchestration
• Monitor pipeline execution
• Troubleshoot pipeline failures

Secure data with Unity Catalog

• Describe Unity Catalog capabilities
• Configure Unity Catalog metastore
• Manage catalogs and schemas
• Secure data assets
• Implement fine-grained access controls

Govern Azure Databricks data assets

• Implement data governance practices
• Manage permissions and roles
• Audit data access
• Apply governance policies
• Manage secure data sharing

Process streaming data in Azure Databricks

• Configure structured streaming workloads
• Process real-time data streams
• Manage checkpoints and triggers
• Monitor streaming queries
• Optimize streaming performance

Deploy and maintain Azure Databricks workloads

• Deploy Azure Databricks solutions
• Configure CI/CD workflows
• Monitor Databricks workloads
• Troubleshoot operational issues
• Optimize performance and cost management