Implement a Data Analytics Solution with Azure Databricks (DP-3011) [M-DP3011]

OVERVIEW

Implement a Data Analytics Solution with Azure Databricks

This course explores how to use Databricks and Apache Spark on Azure to take data projects from exploration to production. You’ll learn how to ingest, transform, and analyze large-scale datasets with Spark DataFrames, Spark SQL, and PySpark, while also building confidence in managing distributed data processing. Along the way, you’ll get hands-on with the Databricks workspace—navigating clusters and creating and optimizing Delta tables.   You’ll also dive into data engineering practices, including designing ETL pipelines, handling schema evolution, and enforcing data quality. The course then moves into orchestration, showing you how to automate and manage workloads with Lakeflow Jobs and pipelines. To round things out, you’ll explore governance and security capabilities such as Unity Catalog and Purview integration, ensuring you can work with data in a secure, well-managed, and production-ready environment.

OBJECTIVES

Students will learn to,

• Explore Azure Databricks
• Perform data analysis with Azure Databricks
• Use Apache Spark in Azure Databricks
• Manage data with Delta Lake
• Build data pipelines with Delta Live Tables
• Deploy workloads with Azure Databricks Workflows
• Use SQL Warehouses in Azure Databricks
• Run Azure Databricks Notebooks with Azure Data Factory

CONTENT

Module 1: Implement a Data Analytics Solution with Azure Databricks

• Explore Azure Databricks
• Perform data analysis with Azure Databricks
• Use Apache Spark in Azure Databricks
• Manage data with Delta Lake
• Build Lakeflow Declarative Pipelines
• Deploy workloads with Lakeflow Jobs