DP-3014: Implementing a Machine Learning solution with Azure Databricks

Azure Databricks is a cloud-scale platform for data analytics and machine learning. In this 1-day course you will learn how to use Azure Databricks to implement machine learning solutions at scale.


Audience / Prerequisites

You should be an Azure data engineer with subject matter expertise in designing, implementing, managing, and maintaining Microsoft Azure Virtual Desktop experiences and remote apps for any device.

Course content

Module 1: Explore Azure Databricks

Provision an Azure Databricks workspace.
Identify core workloads and personas for Azure Databricks.
Describe key concepts of an Azure Databricks solution.

Module 2: Use Apache Spark in Azure Databricks

Describe key elements of the Apache Spark architecture.
Create and configure a Spark cluster.
Describe use cases for Spark.
Use Spark to process and analyze data stored in files.
Use Spark to visualize data.

Module 3: Use Delta Lake in Azure Databricks

Describe core features and capabilities of Delta Lake.
Create and use Delta Lake tables in Azure Databricks.
Create Spark catalog tables for Delta Lake data.
Use Delta Lake tables for streaming data.

Module 4: Use SQL Warehouses in Azure Databricks

Create and configure SQL Warehouses in Azure Databricks.
Create databases and tables.
Create queries and dashboards.

Module 5: Run Azure Databricks Notebooks with Azure Data Factory

Describe how Azure Databricks notebooks can be run in a pipeline.
Create an Azure Data Factory linked service for Azure Databricks.
Use a Notebook activity in a pipeline.
Pass parameters to a notebook.


This one-day, instructor-led training will help you prepare for exam DP-203: Data Engineering on Microsoft Azure.