DP-3011: Implement a data lakehouse analytics solution with Azure Databrick

Course Information

Course Name

DP-3011: Implement a data lakehouse analytics solution with Azure Databricks

Duration

1 Day

Overview

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.

Audience Profile

Before taking this course, learners should already be comfortable with the fundamentals of Python and SQL. This includes being able to write simple Python scripts and work with common data structures, as well as writing SQL queries to filter, join, and aggregate data. A basic understanding of common file formats such as CSV, JSON, or Parquet will also help when working with datasets. In addition, familiarity with the Azure portal and core services like Azure Storage is important, along with a general awareness of data concepts such as batch versus streaming processing and structured versus unstructured data. While not mandatory, prior exposure to big data frameworks like Spark, and experience working with Jupyter notebooks, can make the transition to Databricks smoother.

Prerequisities

At Course Completion

Course Outline

Module 1: Explore Azure Databricks

Module 2: Perform data analysis with Azure Databricks

Module 3: Use Apache Spark in Azure Databricks

Module 4: Manage data with Delta Lake

Module 5: Build data pipelines with Delta Live Tables

Module 6: Deploy workloads with Azure Databricks Workflows

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