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FIRST EDITION 2021 Copyright © BPB Publications, India ISBN: 978-93-89898-

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About the Author

Prashila Naik likes to call herself a writer and technologist. She has over 16 years of experience working in data and its exploits. She has seen data and analytics grow from strength to strength and thinks that it will always be one of the most interesting areas in technology. From being a reluctant data professional to being a seasoned technologist in the space, she feels humbled and proud of her rather long journey.

As a writer, she primarily writes fiction and some creative non-fiction along with translation. This is her first attempt at writing a technology-related book, and she enjoyed the experience just as much as her creative writing pursuits.

About the Reviewer

Ajay Bhaskar is a Microsoft Certified Azure Administrator Associate. As a DevOps Engineer, he has worked on designing and developing solutions for both on-premises and on-cloud CI/CD implementation for several technology stacks (Dotnet, Java, C, C++, Android, iOS, Node/React JS, PHP, etc.) using well-known open source as well as commercial tools such as Microsoft Azure, Azure App Services, Azure DevOps, Jenkins, SonarQube, Proget, JFrog Artifactory, Docker Registry, BuildMaster, Distributed Version Control Systems, and so on. He has good experience with Docker and dockerizing applications into the container, deploying them on the Kubernetes cluster, and managing multiple clusters with Rancher Labs. He is also proficient in creating automation scripts for various tasks in Bash, PowerShell, and Python.

Preface

Microsoft has been a leader in technology for many decades now. Many of us reading this book must have bought our first computer that ran on some form of a Windows operating system. As infrastructure began to move from on-premises to cloud and many industry leaders joined the bandwagon, including Microsoft. Microsoft’s cloud offering is called Azure, and they have evolved into a formidable player with an ever-increasing and ever- growing presence across the world.

Azure offers different kinds of ‘services’ to meet different kinds of needs. These are not just limited to servers and services for traditional server-based web applications or databases, but a highly evolving suite of services for analytics. And now that we have mentioned analytics, let’s stop and look at this word. Analytics is the analysis of data and using the results from it to discover something useful. Traditionally, this was through some simple reports and some statistical measures. But with the advent of big data, and more and more advancements in technology and the web, the area of analytics has undergone a massive change.

Moving beyond what even the likes of traditional data warehouses could do, analytics has enabled not just describing data, but using it to predict and prescribe. Analytics has also moved from the world of batch processing into the realm of more real-time analytics enabling these descriptions, predictions, and prescriptions to all get generated in real-time. This is not it though.

The Internet of Things has opened up another vast (really vast) avenue of enabling inanimate objects with sensors to capture information that can be used to act on these objects to avoid potential issues. This sensor data can be used for analytics as well as for historical analysis.

With such interesting and powerful data all around, a robust technology partner is needed to provide infrastructure, user experience, scale, security, and many other things to turn the data dream into a reality. Given Azure’s track record and cloud capabilities, it turns out to be a great choice to consider for analytics as well.

The primary goal of this book is to provide information about the services in Azure that will provide the functionality to perform the end to end activities involved in analytics. Over the 31 chapters in this book, you will learn the following:

Chapter 1 talks about data and how it is all around us and how, when sourced from the right places and used in the right manner, it can be a huge boost to any organization.

Chapter 2 talks about analytics, summarizes the different types of analytics, and shares basic insights on them from a data science perspective.

Chapter 3 introduces the fascinating Internet of Things and its components and uses.

Chapter 4 will brush up on what artificial intelligence is and its different types. It will also cover what specifically constitutes machine learning and why this is an important area.

Chapter 5 talks about the importance of cloud computing and why the cloud is clearly the future.

Chapter 6 describes a data lake and its importance in the context of big data and analytics. It will also talk about a data lake’s components and how a modern datamart/data warehouse looks like.

Chapter 7 introduces the various types of services in Azure and what their uses are. This will give you the first glimpse of the analytics-related offerings from Azure.

Chapter 8 describes the different types of data and their characteristics.

Chapter 9 will introduce you to Azure Data Factory and how it can be used the for orchestration and ingestion of data.

Chapter 10 describes - how Azure Stream Analytics can be used to achieve this functionality.

Chapter 11 describes Azure Data Lake Storage Gen 2 and how it can offer data lake capabilities on the cloud.

Chapter 12 discusses Azure’s graph db Cosmos DB and its capabilities and features.

Chapter 13 discusses Synapse Analytics and its capabilities and features.

Data science VMs.

Chapter 27 will describe Azure Functions and its uses.

Chapter 28 will talk about Azure Containers, Azure Container Registry, and where it can be used.

Chapter 29 will talk about Azure Containers, Azure Kubernetes Services, and its relevance to analytics.

Chapter 30 will talk about a real-life use case of analytics with a fictional organization and which Azure components would be used.

Chapter 31 will talk about a real-life use case of analytics with a fictional organization and what Azure components would be used.

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Table of Contents

1. Data and its Power Structure Objective 1.1 Introduction to data 1.2 Types of data 1.3 Characteristics of data 1.4 Where does data gets generated 1.5 How different datasets link to each other to produce more data Conclusion Multiple choice questions Answers 2. Evolution of Analytics and its Types Structure Objective 2.1 Reporting and visualization 2.2 Descriptive analytics 2.2 Diagnostic analytics 2.3 Predictive analytics 2.4 Prescriptive analytics 2.5 Other types of machine learning Conclusion Multiple choice questions Answers 3. Internet of Things Structure Objective 3.1 IoT 3.2 Sensors 3.3 Gateway 3.4 Edge

6.1 Advent of data lakes 6.2 Data lake high-level architecture 6.3 Data ingestion 6.4 Data storage 6.5 Data transformation/processing 6.6 Data quality 6.7 Data lineage 6.8 Data cataloging 6.9 Auditing 6.10 Logging 6.11 Monitoring 6.12 Orchestration 6.13 Reporting/data visualization 6.14 Virtualization 6.15 Modern data warehouse/datamart Conclusion Multiple choice questions Answers

7. Introduction to Azure Services Structure Objective 7.1 Azure Data Factory 7.2 Azure Virtual Machine 7.3 Azure Synapse Analytics 7.4 Azure BOT Service 7.5 Azure Databricks 7.6 Azure Data Explorer 7.7 Azure Blockchain Service (preview) 7.8 App Service 7.9 Azure Web App 7.10 Azure Data Catalog 7.11 Azure Data Share 7.12 Azure Functions 7.13 Azure DevOps 7.14 Azure DevTest Labs 7.15 Azure SQL Database

7.16 Azure ExpressRoute 7.17 Azure Sentinel 7.18 Azure database for PostgreSQL 7.19 Azure IoT Hub 7.20 Azure IoT Edge 7.21 Azure Backup 7.22 Azure Maps 7.23 Azure Content Delivery Network (CDN) 7.24 Azure Active Directory 7.25 Azure Machine Learning 7.26 Azure Stream Analytics 7.27 Azure Time Series Insights 7.28 Azure Cosmos DB 7.29 Azure Advisor 7.30 Azure Automation 7.31 Azure Cognitive Search 7.32 Computer Vision 7.33 Face 7.34 Content moderator 7.35 Azure Data Lake Storage 7.36 Azure Analysis Service 7.37 Logic apps 7.38 Azure API for FHIR 7.39 Azure Database Migration Service 7.40 Azure Cache for Redis 7.41 Event Grid 7.42 Azure SQL Database Edge (Preview) Conclusion Multiple choice questions Answers

8. Types of Data Structure Objective 8.1 Traditional operational systems like Enterprise Resource Planning (ERP) 8.2 Sensor data