Using Azure Databricks to Import and Analyze Data

Lab Steps

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Logging in to the Microsoft Azure Portal
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Adding Customer Data to Azure Data Lake Store
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Creating an Azure Databricks Workspace
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Creating a Spark Cluster and Python Notebook in Azure Databricks
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Importing Azure Data Lake Storage Data into Databricks
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Interacting with Data in Azure Databricks

Ready for the real environment experience?

DifficultyBeginner
Time Limit1h
Students1246
Ratings
4.7/5
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Description

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. In addition to it working with most providers, companies use Spark because it uses in-memory computing among other optimizations to offer very fast analytics. Azure Databricks enables companies to integrate their data analytics solutions into their existing Azure infrastructure. In this lab, you'll load data into Azure Data Lake Store and use Databricks to interact with that data through a Databricks workspace and cluster that you'll configure.

Learning Objectives

Upon completion of this lab you will be able to:

  • Load data into Azure Data Lake Store
  • Create and manage a Databricks workspace
  • Create and manage a Databricks cluster
  • Mount data into a Databricks workspace from Azure Data Lake Store
  • Interact with data using Databricks

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:

Updates

September 7th, 2021 - Updated instructions and images to reflect the latest portal experience

June 15th, 2021 - Updated the instruction to reflect the latest portal experience

June 22nd, 2020 - Clarified the format of the Azure Data Lake Storage URL and included a screenshot to avoid confusion 

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About the Author
Students56233
Labs41
Learning paths3

Matt has worked for multiple Fortune 500 companies as a DevOps Engineer and Solutions Architect. He is an AWS Certified DevOps Engineer - Professional, and an AWS Certified Solution Architect - Associate. He enjoys reading and learning new technologies.