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

Course Code: DP-750

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Duration: 4 days

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Course Overview

Learn to implement scalable data engineering solutions using Azure Databricks and lakehouse architecture, from environment setup and governance through data processing, pipeline deployment and production monitoring.

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Target Audience

This course is intended for data engineers who work with SQL and Python and need to design, build and maintain enterprise data pipelines and lakehouse workloads in Azure Databricks.

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Course Objectives

• Configure Azure Databricks environments and compute.
• Secure and govern data using Unity Catalog.
• Ingest, transform and model data using SQL and Python.
• Build scalable lakehouse data pipelines.
• Deploy, monitor and optimise production workloads.
• Integrate Databricks with core Azure services.

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Course Content

Module 1: Set up Azure Databricks

• Architecture, integrations and compute
• Unity Catalog objects

Module 2: Secure and govern Unity Catalog

• Access control and governance

Module 3: Prepare and process data

• Data modelling and ingestion
• Transformation, loading and data quality

Module 4: Deploy and maintain data workloads

• Data pipelines and Lakeflow Jobs
• Development lifecycle
• Monitoring, troubleshooting and optimisation

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Course Prerequisites

A basic understanding of data and analytics concepts is recommended. Experience with SQL, Python, Databricks, cloud storage and Git is helpful.

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Test Certification

Recommended preparation for Exam DP-750: Implementing Data Engineering Solutions Using Azure Databricks, leading to Microsoft Certified: Azure Databricks Data Engineer Associate.

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Note - This course is delivered via our training partner Global Knowledge.