Introduction

A data warehouse is a centralized relational database that integrates data from one or more disparate sources. Data warehouses store current and historical data and are used for reporting and analysis of the data. Many organizations need to store and process data more quickly and easily at an increasing scale to meet business demand and respond to market shifts. In the past this has involved complex processes, using different technologies including Big Data technologies and data integration tools to ingest and prepare the data before presenting it to applications.

Azure Synapse Analytics is designed to simplify this process with limitless scale through one experience.

Assume you are a Data Engineer for Contoso. Your on-premises business intelligence solution consistently completes around 11:00 AM every morning. The Database Administrator has tried everything to improve performance including scaling up and tuning the servers as far as they can go, but the impact has been minimal. Report generation will complete by 10:00 AM at the earliest. As a Data Engineer, you have been tasked to determine how Azure Synapse Analytics can improve the situation and help the database administrator set it up for your company.

Learning Objectives

In this module you will:

  • Explain Azure Synapse Analytics
  • List the types of solution workloads
  • Explain Massively Parallel Processing Concepts
  • Compare Table Geometries
  • Create an Azure Synapse Analytics Service