Introduction

Completed

Many organizations have spent the last two decades building data warehouses and business intelligence (BI) solutions based on relational database systems. Many BI solutions have lost out on opportunities to store unstructured data due to cost and complexity in these types of data and databases.

Suppose that you're a data engineering consultant doing work for Contoso. They're interested in using Azure to analyze all of their business data. In this role, you'll provide guidance on how Azure can enhance their existing business intelligence systems. You'll also offer advice about using Azure's storage capabilities to store large amounts of unstructured data to add value to their BI solution. Because of their needs, you plan to recommend Azure Data Lake Storage. Data Lake Storage provides a repository where you can upload and store huge amounts of unstructured data with an eye toward high-performance big data analytics.

Learning objectives

In this module you will:

  • Learn about Azure Data Lake Storage
  • Create an Azure Storage Account by using the Azure portal
  • Compare Data Lake Storage Gen2 and Azure Blob Storage
  • Explore the stages for processing big data by using Data Lake Storage
  • Review the use cases for Data Lake Storage