Ready for the real environment experience?
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.
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
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
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
July 2nd, 2020 - Updated "Mounting ADLS onto Azure Databricks" lab step to reflect the actual output of the
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.