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

Exploring the analytical data store

After the ingestion process, the result set is stored in an analytical data store, which can be a relational database (the default in data warehouse solutions) or a standard object store in big data lakes.

It is important to evaluate these two types of storage. Let’s discuss them in detail.

Data warehouse

A data warehouse is a relational database with a predefined schema, designed for data analysis purposes rather than transactional workloads.

Analytics databases are typically denormalized in a scheme in which numeric values are stored in central fact tables, which are linked to one or more dimension tables that represent entities that can be aggregated.

A fact table can, for example, contain sales order data that can be grouped by customer, product, store, and time (allowing you, for example, to easily find the total monthly sales revenue per product for each store).

The star schema is a type of fact and dimension table...