MC Microsoft Certified Azure Data Fundamentals Study Guide

Exam DP-900

Jake Switzer

• Publisher: Sybex
• Year: 2022
• Print ISBN:
  9781119855835
• Edition: 1
• eBook ISBN:
  9781119855842
• Topic: Computer Science
• Subtopic: Certification Guides in Computer Science
• Index: Computer Science

The most authoritative and complete study guide for people beginning to work with data in the Azure cloud

In MC Azure Data Fundamentals Study Guide: Exam DP-900, expert Cloud Solution Architect Jake Switzer delivers a hands-on blueprint to acing the DP-900 Azure data certification. The book prepares you for the test – and for a new career in Azure data analytics, architecture, science, and more – with a laser-focus on the job roles and responsibilities of Azure data professionals.

You'll receive a foundational knowledge of core data concepts, like relational and non-relational data and transactional and analytical data workloads, while diving deep into every competency covered on the DP-900 exam. You'll also get:

• Access to complimentary online study tools, including hundreds of practice exam questions, electronic flashcards, and a searchable glossary
• Additional prep assistance with access to Sybex's superior interactive online learning environment and test bank
• Walkthroughs of skills and knowledge that are absolutely necessary for current and aspiring Azure data pros in introductory roles

Perfect for anyone just beginning to work with data in the cloud, MC Azure Data Fundamentals Study Guide: Exam DP-900 is a can't-miss resource for anyone prepping for the DP-900 exam or considering a new career working with Azure data.

Chapter 1
Core Data Concepts

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MICROSOFT EXAM OBJECTIVES COVERED IN THIS CHAPTER:

• Describe types of core data workloads.
  • Describe batch data.
  • Describe streaming data.
  • Describe the difference between batch and streaming data.
  • Describe the characteristics of relational data.
• Describe data analytics core concepts.
  • Describe data visualization (e.g., visualization, reporting, business intelligence (BI).
  • Describe basic chart types such as bar charts and pie charts.
  • Describe analytics techniques (e.g., descriptive, diagnostic, predictive, prescriptive, cognitive).
  • Describe ELT and ETL processing.
  • Describe the concepts of data processing.

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This chapter will focus on the first objective for the Microsoft Azure DP-900 exam certification: describe core data concepts. We will discuss the different types of data and how they are stored, data processing techniques, and categories of data analytics. Understanding the concepts covered in this chapter is critical to designing the most appropriate modern data solution in Azure for any business problem.

Describe Types of Core Data Workloads

The volume of data that the world has generated has exploded in recent years. Zettabytes worth of data is created every year, the variety of which is seemingly endless. Competing in a rapidly changing world requires companies to utilize massive amounts of data that they have only recently been exposed to. What's more is that with the use of edge devices that allow Internet of Things (IoT) data to seamlessly move between the cloud and local devices, companies can make valuable data-driven decisions in real time.

It is imperative that organizations leverage data when making critical business decisions. But how do they turn raw data into usable information? How do they decide what is valuable and what is noise? With the power of cloud computing and storage costs growing cheaper and cheaper every year, it's easy for companies to store all the data at their disposal and build creative solutions that combine a multitude of different design patterns. For example, modern data storage and computing techniques allow sports franchises to create more sophisticated training programs by combining traditional statistical information with real-time data captured from sensors that measure features such as speed and agility. E-commerce companies leverage click-stream data to track a user's activity while on their website, allowing them to build custom experiences for customers to reduce customer churn.

The exponential growth in data and the number of sources organizations can leverage to make decisions have put an increased focus on making the right solution design decisions. Deciding on the most optimal data store for the different types of data involved and the most optimal analytical pattern for processing data can make or break a project before it ever gets started. Ultimately, there are four key questions that need to be answered when making design decisions for a data-driven solution:

• What value will the data powering the solution provide?
• How large is the volume of data involved?
• What is the variety of the data included in the solution?
• What is the velocity of the data that will be ingested in the target platform?

Data Value

The first question that needs to be answered when designing a data-driven solution is, what value will be gained by processing, storing, and analyzing potential data sources? What answers are the business trying to solve? While it is true that having more data can provide new and more fine-grained insights, it can sometimes come at a cost. Organizations must give considerable thought to what data is valuable and what data is not, all the while trying to minimize the amount of time spent in the decision-making process.

Designing a data-driven solution requires everyone involved to focus on deriving value from every process in the solution. This means that data architects must know the business goal of the solution from the beginning. Is this going to be a transactional database that provides the backend for a business's e-commerce site? Will it be a data warehouse aggregating data from multiple source systems to provide a holistic view of a business's performance? Or will the data store need to be able to ingest bursts of IoT data for real-time analytics? To answer these questions, we first need to understand the different types of data stores and the scenarios for which each one is best suited.

Relational Databases

Relational databases organize data into tables that can be linked based on data common to each other. The relationship between tables allows users to easily query multiple tables in the same query by joining columns from multiple tables together. Database tables store data as rows and are organized into a set number of columns. Columns are defined by specific data types such as integer or string so that only specific types of data from new or modified rows of data is accepted. For example, if you have a database table with a name column that only accepts string values, then trying to insert a number into that column will fail. Relational databases allow designers to go a step forward and design constraints on columns so that data must meet predefined criteria. This predefined structure that data in relational databases must adhere to is called a schema and is fundamental to how users query relational data.

Users querying a relational database use a version of the Structured Query Language (SQL) to issue queries to the database. Depending on the vendor, most relational database management systems (RDBMSs) have their own variation of SQL that are based on the ANSI standardized version of SQL. For example, the Microsoft suite of RDBMSs (e.g., SQL Server, Azure SQL Database, Azure SQL Managed Instance) can be interacted with using Transact SQL (T-SQL). T-SQL provides four flavors of commands for query development:

• Data Manipulation Language (DML) commands are used to manipulate data in database tables. DML commands include SELECT, INSERT, UPDATE, and DELETE.
• Data Definition Language (DDL) commands are used to define RDBMS objects such as databases, tables, views, stored procedures, and triggers. DDL commands include CREATE, ALTER, and DROP.
• Data Control Language (DCL) commands are used to manage permissions and access control for users in a database. DCL commands include GRANT, REVOKE, and DENY.
• Transaction Control Language (TCL) commands are used to explicitly manage and control transaction execution to ensure that a specific transaction is successfully done without violating database integrity. TCL commands include BEGIN TRANSACTION, COMMIT TRANSACTION, and ROLLBACK TRANSACTION.

Relational database design considerations largely depend on what the database will be supporting. A database that's supporting a business's e-commerce site and needs to log every transaction made by a customer has vastly different requirements than a database that supports a report application. While there are many different design patterns for data-driven solutions, most of them fall into one of two broad categories: transactional processing systems or analytical systems.

Transactional Processing Systems

Transactional processing systems, also known as online transaction processing (OLTP) systems, are used to capture the business transactions that support the day-to-day operations of an organization. Transactions can include retail purchases logged to point-of-sale (PoS) systems as purchases are made, orders purchased through e-commerce platforms, or even ticket scans at a sport or concert venue. Transactions do not only consist of newly inserted data, but also include deletes and updates of data. W...

Table of contents

• Cover
• Table of Contents
• Title Page
• Copyright
• Acknowledgments
• About the Author
• About the Technical Editor
• Introduction
• Chapter 1: Core Data Concepts
• Chapter 2: Relational Databases in Azure
• Chapter 3: Nonrelational Databases in Azure
• Chapter 4: File, Object, and Data Lake Storage
• Chapter 5: Modern Data Warehouses in Azure
• Chapter 6: Reporting with Power BI
• Appendix: Answers to the Review Questions
• Index
• End User License Agreement