Azure Data Fundamentals A Guide to DP-900 Certification and Beyond (Michael John Peña)（Z-Library）

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Michael John Peña Azure Data Fundamentals A Guide to DP-900 Certif ication and Beyond

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ISBN: 978-1-098-16473-7 US $59.99 CAN $74.99 CLOUD COMPUTING Learn the essential skills and concepts for working with data in the cloud using Microsoft Azure. With this practical guide, professionals new to data management and Azure will learn how to leverage Azure services such as Azure Cosmos DB, Azure Storage, Azure SQL, and Microsoft Fabric to create, store, process, analyze, and visualize data. Author Michael John Peña, principal data and application engineer, also shows you how to apply security, monitoring, and optimization techniques to your data solutions in Azure. Ideal as a resource for the Azure Data Fundamentals certification, this book also provides knowledge you can apply in your daily work even after platforms evolve and new technologies emerge. You’ll gain insights that will help you apply the fundamentals of data operations and engineering in the Azure ecosystem. • Learn core data concepts and the corresponding Microsoft Azure data services • Discover how to work with relational and nonrelational data on Azure • Build data analytics solutions using various Azure data services • Identify and describe core data workloads and data analytics workloads on Azure • Articulate how to work with data security and privacy on Azure • Explain the use cases, benefits, and limitations of different Azure data services Michael John Peña is a principal data and application engineer who thrives on innovation, leadership, and learning. With 15+ years of experience in cloud, big data, IoT, and AI, he’s recognized as a tech leader and community contributor. MJ also cofounded a crypto startup, has held the roles of CTO and solutions architect, and is a Microsoft Data Platform and AI Services MVP. Azure Data Fundamentals “Michael Peña delivers an exceptional bridge between Azure certification and real-world application. His clear, scenario-driven approach equips both newcomers and practitioners to build meaningful, enterprise-ready data solutions on Microsoft’s cloud—precisely what today’s organizations need.” Gary Csaniz, growth partner, The AES Group “In Azure Data Fundamentals, Peña offers an insightful companion to Microsoft Learn’s DP-900 content, drawing on his wealth of experience, and sharing practical knowledge with real-world examples in a clear, engaging, and well-structured way.” Jim McLeod, data solutions engineer, Microsoft

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Praise for Azure Data Fundamentals An outstanding guide for anyone beginning their Azure data journey—clear, structured, and full of real-world context. This book demystifies core concepts and builds a solid foundation for both certification and modern cloud data work. —Fisnik Doko, senior cloud & AI architect, vector8; Microsoft Certified Trainer The Azure Data Fundamentals exam (DP-900) can be challenging because it covers such a breadth of topics. Michael has done a great job of covering so many topics in an up-to- date way that will help you to pass this exam. I particularly liked the chapter summaries that let you focus on the key points of each chapter and how they relate to the exam. And a special bonus was the hints that Michael has sprinkled throughout the book, which help you to know what to look for in exam questions. —Dr. Greg Low, founder and principal mentor for SQL Down Under; long-term data platform MVP and member of the Microsoft Regional Director program Navigating the new world of data and AI demands a foundational understanding of data’s core elements. Azure Data Fundamentals masterfully distills these complex concepts— from core relational concepts to Azure cloud workloads—into an easily understandable starting point, giving everyone the fundamental know-how to build their data future. —Rodney Joyce, managing director, Data-Driven This is a great book, not just for preparing for the DP-900 exam, but for learning about Azure Data in general. In particular, I love the “Beyond the Exam” sections at the end of each chapter, which give practical advice to anyone getting into this space. —Rob Farley, owner and principal consultant, LobsterPot Solutions

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Michael’s extensive experience in Data, AI, and Azure is seen through in this book. He makes it easy to understand how to get started with these technologies, whether you’re a beginner starting your career in tech, or a seasoned professional looking to refine, refresh, or re-learn your skills. His practical insights and clear explanations make this an invaluable resource for anyone passionate in tech, especially data. —Philip Domingo, account technology strategist, Microsoft It is rare to find someone with Michael’s deep expertise in data and AI who writes for beginners. He distills decades of real-world experience into crystal-clear fundamentals. This book is a must-read for anyone seeking to master modern data practices and ace the exam. —David Ding, lead data consultant, SDInnovation Pty Ltd; author; Microsoft MVP Working with Michael over the years has really shown me how uniquely he can take deep data and AI concepts and make them feel simple and approachable. He has a knack for explaining things in a way that just makes sense, always backed by real experience rather than theory. This guide reflects exactly that, giving readers a solid and practical starting point for understanding Azure’s data ecosystem. —Bryan Anthony Garcia, senior consultant, Versent Michael provides the key to passing the DP-900, with bonus best practices and patterns that go beyond the exam, all in a single book. A must study for those that are looking to jumpstart their career in data or looking to refresh on Azure data fundamentals. —Miles Cole, principal program manager, Microsoft A rare guide that balances exam preparation with real-world utility. This book offers practical tips and foundational knowledge that every data engineer needs, making it useful long after you’ve passed the DP-900. —Rafferty Uy, cloud infrastructure manager in the financial sector

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Michael John Peña Azure Data Fundamentals A Guide to DP-900 Certification and Beyond

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978-1-098-16473-7 [LSI] Azure Data Fundamentals by Michael John Peña Copyright © 2026 The Trustee for Repollo Peña Investment Trust. All rights reserved. Published by O’Reilly Media, Inc., 141 Stony Circle, Suite 195, Santa Rosa, CA 95401. O’Reilly books may be purchased for educational, business, or sales promotional use. Online editions are also available for most titles (http://oreilly.com). For more information, contact our corporate/institutional sales department: 800-998-9938 or corporate@oreilly.com. Acquisitions Editor: Megan Laddusaw Development Editor: Rita Fernando Production Editor: Christopher Faucher Copyeditor: Audrey Doyle Proofreader: Dwight Ramsey Indexer: Krsta Technology Solutions Cover Designer: Susan Thompson Cover Illustrator: Karen Montgomery Interior Designer: David Futato Interior Illustrator: Kate Dullea December 2025: First Edition Revision History for the First Edition 2025-12-08: First Release See http://oreilly.com/catalog/errata.csp?isbn=9781098164737 for release details. The O’Reilly logo is a registered trademark of O’Reilly Media, Inc. Azure Data Fundamentals, the cover image, and related trade dress are trademarks of O’Reilly Media, Inc. The views expressed in this work are those of the author and do not represent the publisher’s views. While the publisher and the author have used good faith efforts to ensure that the information and instructions contained in this work are accurate, the publisher and the author disclaim all responsibility for errors or omissions, including without limitation responsibility for damages resulting from the use of or reliance on this work. Use of the information and instructions contained in this work is at your own risk. If any code samples or other technology this work contains or describes is subject to open source licenses or the intellectual property rights of others, it is your responsibility to ensure that your use thereof complies with such licenses and/or rights.

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Table of Contents Preface. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii Part I. Core Data Concepts 1. Introduction to Data Representation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Understanding the Data Spectrum 4 Structured Data 5 Characteristics of Structured Data 6 Common Examples of Structured Data 6 Use Cases in Azure Environments 7 Benefits and Limitations of Structured Data 7 Semi-Structured Data 8 Characteristics of Semi-Structured Data 9 Common Examples of Semi-Structured Data 9 Use Cases in Azure Environments 11 Benefits and Limitations of Semi-Structured Data 12 Unstructured Data 13 Characteristics of Unstructured Data 14 Common Examples of Unstructured Data 15 Use Cases in Azure Environments 16 Benefits and Limitations of Unstructured Data 17 Bringing It All Together: Data Representation in Practice 18 Making an Informed Decision About Data Representation 19 Implementing Evolution and Adaptation Strategies 20 Summary 21 Beyond the Exam 23 Common Misconceptions About Data Types and When to Use Each 23 v

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Real-World Examples of Data Type Decisions Gone Wrong 24 Practical Tips for Evaluating Data Structure Needs 24 How to Future-Proof Your Data Representation Choices 25 2. Data Storage Fundamentals. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 Data File Format Essentials 28 CSV: The Universal Translator 29 JSON: The Modern Standard 31 Parquet: The Analytics Powerhouse 33 Avro: The Evolution Engine 35 How to Choose the Right Format for Your Scenario 36 Types of Databases: Choosing Your Data Foundation 36 Relational Databases: The Reliable Foundation 37 NoSQL Databases: The Flexibility Champions 40 Data Warehouses: The Analytics Specialists 42 Cloud Versus On-Premises Storage: The Strategic Choice 43 Advantages of Cloud Storage 44 Considerations for Cloud Storage 46 Advantages of On-Premises Storage 47 Considerations for On-Premises Storage 48 Azure Hybrid Solutions 49 Summary 50 Beyond the Exam 52 Choosing the Right Storage Solution: A Practical Decision Framework 52 Identifying the Hidden Costs in Cloud Storage: What to Watch For 53 Future-Proofing Your Data Storage Choices 53 3. Understanding Data Workloads. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 Data Workloads in Everyday Terms 56 The Workload Spectrum 57 Transactional Workloads (OLTP) 59 Analytical Workloads (OLAP) 65 Bringing It All Together: Modern Workload Scenarios 71 The Modern Data Architecture 71 A Comprehensive Real-World Example 72 Summary 73 Beyond the Exam 74 Real-World Mixed Workload Examples 74 Performance Considerations 75 Common Implementation Pitfalls 75 vi | Table of Contents

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4. Data Roles and Responsibilities in Azure. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77 Modern Data Roles 77 Database Administrators 78 Data Engineers 81 Data Analysts 83 Data Scientists 86 Data Governors 88 Bringing It All Together: Role Interactions in Practice 90 Summary 91 Beyond the Exam 92 Real-World Role Dynamics 92 Career Progression and Emerging Roles 93 Organizational Impact 93 Future Outlook 93 Part II. Relational Data on Azure 5. Relational Data Concepts. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97 Core Relational Concepts 97 Understanding Tables 99 Data Types and Constraints 101 Understanding Relationships and Keys 102 Types of Table Relationships 102 Use of Foreign Keys to Establish Table Relationships 103 A Practical Example 104 Basic SQL Queries 105 Retrieving Data with SELECT 106 Modifying Data 108 Joining Tables 110 Common Database Objects 112 Views: Simplifying Data Access 112 Indexes: Improving Data Retrieval 114 Stored Procedures: Creating Reusable Code 115 Triggers: Creating Automatic Responses 117 Bringing It All Together: The Library Database in Practice 118 Basic Banking Database Structure 119 Common Database Operations 120 A Practical Example 122 Summary 123 Beyond the Exam 124 Modern Relational Database Evolution 124 Table of Contents | vii

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Implementation Realities 125 The Scale Challenge 126 Emerging Directions 127 6. Azure SQL Services and Open Source Options. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129 Understanding the Azure SQL Family 129 Azure SQL Database: Cloud Native Database Solution 130 Azure SQL Managed Instance: The Bridge to the Cloud 132 SQL Server on Azure Virtual Machines: Maximum Control and Flexibility 133 Exploring Open Source Databases in Azure 134 Azure Database for MySQL: Powering Web Applications 134 Azure Database for PostgreSQL: Enterprise-Grade Open Source 136 Bringing It All Together: Choosing the Right Database Service 137 Starting with the Basics 138 Understanding Your Workload 138 Thinking About Growth 138 Simplifying Cost Considerations 139 Summary 139 Beyond the Exam 140 Making the Right Choice in Practice 140 Navigating Cost Management 140 Understanding Differences in Implementation 141 Looking Ahead 141 Part III. Nonrelational Data on Azure 7. Azure Storage Solutions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145 Azure Blob Storage 146 Understanding Object Storage 147 Types of Blobs 148 Access Tiers and Cost Management 150 Security Features of Blob Storage 153 Managing the Data Lifecycle 155 Azure File Storage 155 Understanding Cloud File Shares 156 SMB and NFS: Speaking Your Language 156 Advanced Features That Transform File Sharing 157 Azure File Sync: Extending to Hybrid Scenarios 159 Managing at Scale 161 Azure Table Storage 161 Understanding NoSQL in Azure Table Storage 162 viii | Table of Contents

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Entities and Properties: A Flexible Foundation 162 Partitioning Strategy: The Key to Performance 163 Query Patterns and Performance 164 Bringing It All Together: Storage Solutions in Practice 165 The Modern Data Estate 166 Integration Patterns in Action 166 Bridging Worlds with Hybrid Solutions 166 The Art of Cost Optimization 166 Summary 167 Beyond the Exam 168 Modern Storage Evolution 169 Implementation Realities 169 The Scale Challenge 170 Emerging Directions 170 8. Azure Cosmos DB. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 171 Understanding the Cosmos DB Architecture 172 The Power of Global Distribution 172 Understanding Consistency Models 172 Understanding Data Modeling in Azure Cosmos DB 173 Azure Cosmos DB API Types 175 NoSQL (Core) API in Azure Cosmos DB 175 MongoDB API in Azure Cosmos DB 177 Cassandra API in Azure Cosmos DB 179 Table API in Azure Cosmos DB 180 Gremlin API in Azure Cosmos DB 181 Bringing It All Together: Azure Cosmos DB in Practice 182 Embracing the Modern Data Platform 183 Harmonizing Different Data Models 185 Building Connections Through Graph Data 186 Implementing Cost Optimization and Performance Tuning 187 Summary 188 Beyond the Exam 189 The Reality of Global Distribution in the Supply Chain 189 Cost Management in Supply Chain Operations 190 Supply Chain Data Modeling Evolution 190 Integration Challenges in Multiregion Operations 191 Performance Tuning for the Global Supply Chain 191 The Future of Global Supply Chain Technologies 191 Table of Contents | ix

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Part IV. Analytics on Azure 9. Large-Scale Analytics. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 195 Understanding Large-Scale Analytics 196 The Scale Challenge 197 Components of Large-Scale Analytics 198 Data Ingestion and Processing 199 The Ingestion Challenge 199 Batch and Streaming Paradigms 200 Processing Considerations 201 Analytical Data Stores 202 The Storage Challenge 202 Data Lakes 203 Data Warehouses 204 Analytical Databases 205 How to Choose the Right Analytical Store 206 Microsoft Cloud Services for Large-Scale Analytics 207 Azure Databricks 208 Azure Synapse Analytics 209 Microsoft Fabric 211 How to Choose Between Analytics Services 213 Bringing It All Together: Large-Scale Analytics in Practice 214 The Data Landscape 214 The Analytics Architecture 215 Implementation Approach 216 Summary 217 Beyond the Exam 218 The Organizational Factor 219 Implementation Realities 220 The Scale Challenge 221 Emerging Directions 221 10. Real-Time Analytics. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 223 Understanding Real-Time Analytics 225 The Time-Value Relationship of Data 225 The Evolution from Batch to Real-Time Analytics 226 The Architecture of Real-Time Analytics 228 Batch Versus Streaming Data 229 Characteristics of Batch Processing 230 Characteristics of Streaming Data 232 When to Use Each Approach 233 Microsoft Cloud Services for Real-Time Analytics 234 x | Table of Contents

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Azure Event Hubs 235 Azure Stream Analytics 236 Azure Synapse Analytics 239 Azure Data Explorer 240 Azure Event Grid 242 Azure Functions 244 Azure IoT Hub 246 How to Choose the Right Real-Time Analytics Services 248 Bringing It All Together: Real-Time Analytics in Practice 249 The Data Landscape 249 The Analytics Architecture 250 Implementation Approach 251 Summary 252 Beyond the Exam 253 Organizational Readiness 254 Implementation Realities 254 Emerging Directions 255 11. Data Visualization with Power BI. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 257 Understanding Data Visualization 258 The Power of Visual Communication 259 The Evolution of Business Intelligence 259 The Analytical Workflow 260 Power BI Capabilities 262 Power BI Components 262 Connectivity and Data Access 264 Data Transformation and Preparation 265 Data Modeling 266 Visualization and Analysis 267 Sharing and Collaboration 269 Data Models in Power BI 271 Model Structures and Relationships 271 Calculated Columns and Measures 273 Row-Level Security 274 DirectQuery and Import Modes 275 Visualizations in Power BI 277 Choosing the Right Visualization 277 Understanding Dashboard and Report Design 279 Implementing Advanced Visualization Techniques 280 Bringing It All Together: Power BI in Practice 282 The Data Landscape 282 The Power BI Implementation 283 Table of Contents | xi

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The Implementation Approach 284 The Business Outcomes 285 Summary 286 Beyond the Exam 288 Organizational Factors 288 Implementation Realities 289 Emerging Directions 290 Part V. Beyond DP-900 12. Advanced Azure Data Concepts. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 293 The Modern Data Governance Imperative 294 Understanding Microsoft Purview 294 Implementing Data Governance at Scale 296 The Rise of AI and Large Language Models 297 Understanding Large Language Models in Azure 298 Building AI Agents with Azure 300 Practicing Responsible AI and Data Ethics 301 The Convergence: Governed AI 302 Governance-Aware AI Systems 302 Building the Future of Data Work 302 Your Journey Beyond DP-900 303 The Skills That Matter 303 Pathways Forward 304 The Azure Advantage 305 Summary 305 Conclusion: Your Data Future Starts Now 306 Beyond This Book 307 Practical Next Steps 307 How to Stay Current 308 Final Thoughts 308 Index. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 309 xii | Table of Contents

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Preface In a time when AI dominates headlines, it’s easy to forget that intelligent systems rely fundamentally on the quality of their underlying data models, storage decisions, workload designs, and governance practices. This book is about mastering those foundations so that you can build, reason about, and evolve Azure data solutions with confidence. Rather than overloading you with disconnected facts, I’ll use stories, analogies, and real-world mental models—coffee shops, loyalty systems, streaming dashboards, global social apps, role collaboration, and future-facing governance challenges—to help you grasp these concepts. These are not embellishments. They serve as learning anchors to aid recall under exam pressure and in real project conversations. Who Should Read This Book Whether you’re an IT professional, a data analyst, or a data scientist, I wrote this book for you. If you’re studying for the DP-900 certification exam, this could be your new best friend. And hey, even if you’re new to cloud computing and data concepts, don’t worry. We’ll start with the basics before we delve into the more complex stuff. Why I Wrote This Book Azure’s data platform has grown from a few services into an expansive, integrated ecosystem. That breadth creates a paradox: more capability—but also more decision friction. When do you choose a fully managed relational platform versus a distributed NoSQL option? How do workload characteristics influence architecture? How do roles collaborate across ingestion, modeling, analytics, governance, and visualization? I wrote this book to connect those dots through enduring principles rather than short-lived marketing slogans. xiii

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My goals are twofold: 1. Help you pass the DP-900 exam by truly understanding—not memorizing—the conceptual domains. 2. Prepare you to make informed architectural and career decisions in real-world Azure environments. Navigating This Book The book is divided into five parts: Part I, “Core Data Concepts” (Chapters 1–4) We establish the vocabulary: how data is represented; fundamental storage con‐ siderations; distinctions between transactional and analytical workloads; and how evolving data roles collaborate in Azure. You’ll develop a solid intuition before navigating the array of service menus. Part II, “Relational Data on Azure” (Chapters 5 and 6) We dive deep into relational concepts such as normalization, constraints, index‐ ing logic, and integrity, then map them to Azure SQL offerings and managed open source options—highlighting the trade-offs between control, scalability, and operational responsibility. Part III, “Nonrelational Data on Azure” (Chapters 7 and 8) We distinguish between Azure Storage modalities—object, table, queue, file, and core patterns—and then explore Azure Cosmos DB’s globally distributed model, including its consistency, partitioning, replication, and latency and the architec‐ tural shift it represents. Part IV, “Analytics on Azure” (Chapters 9–11) We progress from large-scale batch and lake-centric analytics to real-time streaming patterns, then move into visualization, modeling, and self-service insight delivery with Power BI—bridging raw telemetry data to executive dash‐ boards and data storytelling. Part V, “Beyond DP-900” (Chapter 12) We look beyond the exam to holistic governance, the role of Microsoft Purview, security posture evolution, performance patterns, AI integration, and emerging large language model (LLM)–assisted data interaction. This is your “future readi‐ ness” accelerator. xiv | Preface

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Each chapter follows a consistent structure: 1. A scenario or analogy to establish context 2. Progressive layering of concepts with comparative framing 3. Figures or mental models to aid understanding and retention 4. Decision-making heuristics or selection frameworks How you use this book will depend on your goals: First-time learner Read the book sequentially. Avoid skipping the foundational chapters, as they reduce cognitive load later on. Exam preparation under time pressure Skim the objectives, then attempt quizzes without prior review, and finally revisit the concepts you missed. Experienced practitioner Start with Parts II–IV, then return to Part I for conceptual alignment. Governance/AI curious Read Part I thoroughly, skim Parts II–IV for vocabulary continuity, then focus on Chapter 12. Staying Current Azure evolves. Core patterns—workload classification, consistency trade-offs, gover‐ nance principles, performance thinking—are far more durable than naming changes or UI shifts. Treat this book as your conceptual compass; verify SKU minutiae and pricing in the portal or official docs when implementing. Conventions Used in This Book The following typographical conventions are used in this book: Italic Indicates new terms, URLs, email addresses, filenames, and file extensions. Constant width Used for program listings, as well as within paragraphs to refer to program ele‐ ments such as variable or function names, databases, data types, environment variables, statements, and keywords. Preface | xv

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This element signifies a tip or suggestion. This element signifies a general note. This element indicates a warning or caution. Using Code Examples If you have a technical question or a problem using the code examples, please send email to support@oreilly.com. This book is here to help you get your job done. In general, if example code is offered with this book, you may use it in your programs and documentation. You do not need to contact us for permission unless you’re reproducing a significant portion of the code. For example, writing a program that uses several chunks of code from this book does not require permission. Selling or distributing examples from O’Reilly books does require permission. Answering a question by citing this book and quoting example code does not require permission. Incorporating a significant amount of example code from this book into your product’s documentation does require permission. We appreciate, but generally do not require, attribution. An attribution usually includes the title, author, publisher, and ISBN. For example: “Azure Data Fundamen‐ tals by Michael John Peña (O’Reilly). Copyright 2026 The Trustee for Repollo Peña Investment Trust, 978-1-098-16473-7.” If you feel your use of code examples falls outside fair use or the permission given above, feel free to contact us at permissions@oreilly.com. xvi | Preface

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O’Reilly Online Learning For more than 40 years, O’Reilly Media has provided technol‐ ogy and business training, knowledge, and insight to help companies succeed. Our unique network of experts and innovators share their knowledge and expertise through books, articles, and our online learning platform. O’Reilly’s online learning platform gives you on-demand access to live training courses, in-depth learning paths, interactive coding environments, and a vast collection of text and video from O’Reilly and 200+ other publishers. For more information, visit https://oreilly.com. How to Contact Us Please address comments and questions concerning this book to the publisher: O’Reilly Media, Inc. 141 Stony Circle, Suite 195 Santa Rosa, CA 95401 800-889-8969 (in the United States or Canada) 707-827-7019 (international or local) 707-829-0104 (fax) support@oreilly.com https://oreilly.com/about/contact.html We have a web page for this book, where we list errata and any additional informa‐ tion. You can access this page at https://oreil.ly/azure-data-fundamentals. For news and information about our books and courses, visit https://oreilly.com. Find us on LinkedIn: https://linkedin.com/company/oreilly-media. Watch us on YouTube: https://youtube.com/oreillymedia. Acknowledgments To my wife, Maria—your patience and partnership grounded this entire journey. To my children—thank you for the perspective and energy you bring to every day. To my family and friends—your encouragement turned deadlines into momentum. To Rita, Megan, and Virginia from O’Reilly—thank you for being very patient with me throughout the development and production of this book. To the publishing team—thank you for supporting a concept-first approach. Preface | xvii

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To these awesome book reviewers, who made this book possible by providing excel‐ lent and sound feedback: • Pio Balistoy • John Paul Ada • Caleb Lent • Håkan Silfvernagel To the Microsoft MVP community—your collaborative spirit continually sharpens my thinking. To the Azure product teams—your relentless innovation powers mission-critical systems globally. And to you, the reader: thanks for investing your time here. I hope these pages accel‐ erate your growth and amplify your impact. Let’s dive in. xviii | Preface