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

Understanding the types of non-relational data

Non-relational data is all types of data that we can store in an entity, and is not relationally structured data. This means that any type of document, event, log, photo, or video, among others, can be stored in a non-relational database.

Now, let’s look at the different types of non-relational data.

Non-structured data

Non-structured data is data that does not have queryable formatting and can be from log files without header structures, image files, videos, and audio, among other binary formats. These files are not easily interpreted by query indexing tools, so they are called unstructured.

Before this data becomes information and can assist in decision-making, it is necessary to go through systems that organize the data of these files in a way that facilitates their understanding by the query tools.

Here are two examples of categories of data transcription processes from unstructured files:

  • Speech-to-Text...