Book Image

Microsoft Certified Azure Data Fundamentals (Exam DP-900) Certification Guide

By : Marcelo Leite
5 (1)
Book Image

Microsoft Certified Azure Data Fundamentals (Exam DP-900) Certification Guide

5 (1)
By: Marcelo Leite

Overview of this book

Passing the DP-900 Microsoft Azure Data Fundamentals exam opens the door to a myriad of opportunities for working with data services in the cloud. But it is not an easy exam and you'll need a guide to set you up for success and prepare you for a career in Microsoft Azure. Absolutely everything you need to pass the DP-900 exam is covered in this concise handbook. After an introductory chapter covering the core terms and concepts, you'll go through the various roles related to working with data in the cloud and learn the similarities and differences between relational and non-relational databases. This foundational knowledge is crucial, as you'll learn how to provision and deploy Azure's relational and non-relational services in detail later in the book. You'll also gain an understanding of how to glean insights with data analytics at both small and large scales, and how to visualize your insights with Power BI. Once you reach the end of the book, you'll be able to test your knowledge with practice tests with detailed explanations of the correct answers. By the end of this book, you will be armed with the knowledge and confidence to not only pass the DP-900 exam but also have a solid foundation from which to embark on a career in Azure data services.
Table of Contents (21 chapters)
1
Part 1: Core Data Concepts
7
Part 2: Relational Data in Azure
11
Part 3: Non-Relational Data in Azure
14
Part 4: Analytics Workload on Azure

Describing modern data warehousing

Created in the 80s, the data warehouse (DW) concept is “a subject-oriented, integrated, non-volatile, variable data repository over time to support management decisions” (C. J. Date, Introdução a Sistemas de Bancos de Dados. eighth edition, Rio de Janeiro Campus, 2004).

In other words, a data warehouse is a database that organizes an organization’s information, leaving the data more aligned with the nomenclature of company affairs so that it can be consumed by reports and applications.

Some important concepts when dealing with a data warehouse are as follows:

  • Extract, transform, and load (ETL): This is the technique used to extract the information from the source relational databases, organize this data, and load only the result into the DW.
  • Data mart: This is a logical subset of the data warehouse, usually divided by department, subject, or views required by users.
  • Business intelligence (BI): This...