Azure Databricks is an analytics platform powered by Apache Spark. Spark is a unified analytics engine capable of working with virtually every major database, data caching service, and data warehouse provider.

However, Spark clusters in Databricks also support Scala, since Apache Spark is built on Scala. Scala is a high-level programming language that combines aspects of both functional and object-oriented programming to form a concise language that is especially useful in an environment like Databricks. Using Databricks's built-in support for data analytics with Scala's ability to efficiently interact with resources in a customizable way gives companies a high level of control over their data and analytics.

In this lab, you'll use Scala in an Azure Databricks cluster to interact with Azure Data Lake Storage (ADLS), including ingesting, transforming, and writing data to the store.

Learning Objectives

Upon completion of this lab you will be able to:

• Load data into Azure Data Lake Storage
• Create and manage a Databricks workspace
• Create and manage a Databricks cluster
• Use Scala to manage folders and write data to ADLS
• Use Scala to create DataFrames from data in ADLS

Intended Audience

This lab is intended for:

• Azure administrators
• Cloud engineers and solutions architects
• Data engineers
• Anyone with a need to visualize and analyze data in Azure

Prerequisites

You should be familiar with:

• Basic familiarity with the Azure Portal is helpful, but not required
• The videos on using Azure Databricks to interact with ADLS data are helpful

Updates

October 24th, 2022 - Updated the instructions and screenshots to reflect the latest UI

Nov 3rd, 2021 - Updated instruction to resolve the login issue with Azure Databricks

October 23rd, 2021 - Provide a workaround for an Azure Active Directory issue that initially prevents logging in to Databricks

July 2nd, 2020 - Updated "Mounting ADLS onto Azure Databricks" lab step to reflect the actual output of the ls command