Upcoming dates

All times shown in the class's own timezone. 1 of 1 are guaranteed to run, meaning they go ahead regardless of enrollment.

Dates	Starts	Where	Status	Seat	Book
Mon, Aug 17	9:00 AM to 5:00 PM EDT	Live virtual	GUARANTEED	$695

About this class

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.

Course outline

1 - Explore Azure Databricks

Get started with Azure Databricks
Identify Azure Databricks workloads
Understand key concepts
Data governance using Unity Catalog and Microsoft Purview
Module assessment

2 - Perform data analysis with Azure Databricks

Ingest data with Azure Databricks
Data exploration tools in Azure Databricks
Data analysis using DataFrame APIs
Module assessment

3 - Use Apache Spark in Azure Databricks

Get to know Spark
Create a Spark cluster
Use Spark in notebooks
Use Spark to work with data files
Visualize data
Module assessment

4 - Manage data with Delta Lake

Get started with Delta Lake
Create Delta tables
Implement schema enforcement
Data versioning and time travel in Delta Lake
Data integrity with Delta Lake
Module assessment

5 - Build Lakeflow Declarative Pipelines

Explore Lakeflow Declarative Pipelines
Data ingestion and integration
Real-time processing
Module assessment

6 - Deploy workloads with Lakeflow Jobs

What are Lakeflow Jobs?
Understand key components of Lakeflow Jobs
Explore the benefits of Lakeflow Jobs
Deploy workloads using Lakeflow Jobs
Module assessment

Before you attend

Familiarity with SQL and basic Python
Working knowledge of Azure fundamentals
Basic understanding of data engineering or analytics workflows

Who this is for

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.