Real-Time Intelligence with Microsoft Fabric Empowering Data-Driven Decisions in the Era of AI (Johan Ludvig Brattås, Frank Geisler)（Z-Library）

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Johan Ludvig Brattås & Frank Geisler Foreword by Yitzhak Kesselman Real-Time Intelligence with Microsoft Fabric Empowering Data-Driven Decisions in the Era of AI

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9 7 8 1 0 9 8 1 7 8 5 8 1 5 5 9 9 9 ISBN: 978-1-098-17858-1 US $59.99 CAN $74.99 DATA / BUSINESS INTELLIGENCE TOOL S In today’s hyperconnected world, many organizations are overwhelmed by the volume of data generated every second. Making timely decisions using this information remains a challenge for many. Real-time intelligence has transformed from a luxury to a necessity for businesses striving to stay ahead in a rapidly evolving marketplace. Enter Microsoft Fabric’s Real-Time Intelligence: a new tool that not only analyzes real-time data but also acts upon the results. If you’re ready to unlock the power of immediate insights, this comprehensive primer offers an exploration into the capabilities of Real-Time Intelligence with Microsoft Fabric. Authors Johan Ludvig Brattås and Frank Geisler explain event-driven insights and how to use them to drive business success. Whether you’re a seasoned professional or an enthusiast, this guide is the key to understanding an exciting new platform. You’ll discover: • The core concepts of Real-Time Intelligence within Microsoft Fabric • Challenges that can be solved with Real-Time Intelligence, enhancing efficiency • Techniques for using KQL queries, including SQL knowledge to optimize these queries • Practical applications including data analytic solutions, event streams, and more • How to automatically trigger actions based on data conditions Johan Ludvig Brattås has 25 years of experience in business intelligence and data platforms and a passion for sharing his expertise with the Azure and Power BI community across Norway and beyond. Frank Geisler is a best-selling author of books on Power BI and Azure, a recognized expert on the Microsoft data platform, and a dedicated leader in the tech community. Real-Time Intelligence with Microsoft Fabric “Johan and Frank built the missing on-ramp to Real-Time Intelligence in Fabric. The book escalates layer by layer, featuring a full hands-on project. By the time you finish, you are not reading about real-time intelligence anymore—you are doing it.” Nikola Ilic, principal architect, Microsoft Fabric at iLink Digital, and Microsoft MVP “In this book, Frank and Johan give you not just the technical underpinnings of Real-Time Intelligence in Microsoft Fabric but also the architectural understanding and hands-on examples to help you take your newfound knowledge and turn it into action. Your data estate will thank you.” Matt Gordon, director of data and analytics, LEAN TECHniques, and Microsoft MVP

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Johan Ludvig Brattås and Frank Geisler Foreword by Yitzhak Kesselman Real-Time Intelligence with Microsoft Fabric Empowering Data-Driven Decisions in the Era of AI

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978-1-098-17858-1 [LSI] Real-Time Intelligence with Microsoft Fabric by Johan Ludvig Brattås and Frank Geisler Copyright © 2026 Johan Ludvig Brattås and Frank Geisler. 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. Acquisition Editor: Michelle Smith Development Editor: Rita Fernando Production Editor: Katherine Tozer Copyeditor: Doug McNair Proofreader: Vanessa Moore Indexer: nSight, Inc. Cover Designer: Susan Brown Cover Illustrator: José Marzan Jr. Interior Designer: David Futato Interior Illustrator: Kate Dullea May 2026: First Edition Revision History for the First Edition 2026-05-07: First Release See http://oreilly.com/catalog/errata.csp?isbn=9781098178581 for release details. The O’Reilly logo is a registered trademark of O’Reilly Media, Inc. Real-Time Intelligence with Microsoft Fabric, the cover image, and related trade dress are trademarks of O’Reilly Media, Inc. The views expressed in this work are those of the authors and do not represent the publisher’s views. While the publisher and the authors have used good faith efforts to ensure that the information and instructions contained in this work are accurate, the publisher and the authors 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 Foreword. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi Preface. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xv 1. Introduction to Microsoft Fabric. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Challenges of a Multiproduct Data Analytics Platform 1 Fabric Key Workloads and Capabilities 3 High-Level Architectural Overview 6 Common Data Store 7 Compute Engines 7 Common Metadata Platform 8 REST APIs 8 Security and Governance 9 Data Storage Paradigms 9 Data Warehouses 10 Data Lakehouses 11 Eventhouses 13 Choosing the Right Solution for Your Needs 14 Licensing and Capacities 15 Tenant 16 Capacity 16 Workspace 17 Conclusion 19 2. Business Intelligence Versus Real-Time Intelligence. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 Real Time Versus Near-Real Time 21 Business Intelligence 22 Key Features of Business Intelligence 22 iii

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Business Intelligence Use Cases 23 Real-Time Intelligence 24 Key Features of Real-Time Intelligence 24 Real-Time Intelligence Use Cases 25 Key Differences Between Business Intelligence and Real-Time Intelligence 27 Selecting an Approach 28 Integrating Business Intelligence and Real-Time Intelligence 28 Conclusion 29 3. Real-Time Intelligence in Microsoft Fabric. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 The Components of Real-Time Intelligence 31 The Real-Time Hub 33 Eventhouses 34 KQL Databases 36 KQL Querysets 37 Eventstreams 41 Real-Time Dashboard 43 Activator 45 Conclusion 46 4. A Hands-on Project. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 The Scenario 47 The Coffee Machine Scenario 47 The Architecture of the Solution 48 Building the Medallion Architecture 49 The Data Schema 50 Getting Started 51 Prerequisites 51 Creating a Fabric Workspace 52 Creating a New Eventhouse 55 Enabling OneLake Availability 56 Ingesting and Transforming Data 58 Creating a New Eventstream 59 Importing the Coffee Cast Notebook 61 Running the Coffee Cast Notebook 62 Defining the Eventstream Topology 64 Enriching Data 73 Building a KQL Database Schema for a Medallion Architecture 81 Adding Visualizations and Alerts 88 Building a Real-Time Dashboard 89 Using Data Activator for Alerts 95 Conclusion 103 iv | Table of Contents

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5. Fabric’s Eventstream Service. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105 Key Challenges in Stream Processing 106 Time-Related Complexity 106 State Management 106 Scalability and Fault Tolerance 107 Stream-Processing Methodologies 107 Eventstream Overview 108 Sources and Destinations 108 Dataflow Model 110 Stream Processor 111 Data Transformation and Processing 111 Simple Transformations 114 Advanced Transformations 117 A Hands-on Example 124 The Scenario 124 Getting Started 125 Processing the Data 127 Creating a Real-Time Alert with Activator 136 Bonus Exercise: The SQL Option 141 Conclusion 143 6. Fabric’s Eventhouse Service. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145 Evolution and Purpose of Eventhouses and KQL Databases 145 Key Features of Eventhouse 146 Ribbon Menu 148 Explorer Pane 150 Key Features of KQL Databases 155 Core Architecture and Data Structure 155 Data Ingestion 155 Data Storage and Caching 158 Query Automation 159 Data Integration and External Sources 161 Data Sharing and Replication 165 Anomaly Detection 166 Conclusion 167 7. The Real-Time Hub. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 169 Real-Time Hub Navigation 170 Streaming Data 171 Stream Details 175 Adding Data 177 Data Source Listing 178 Table of Contents | v

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Filters 178 Actions 178 “Subscribe to” 179 Event Schema Registry 182 Conclusion 184 8. Activator. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 185 Introduction to Activator 186 Architectural Overview 187 Integration with the Fabric Ecosystem 187 Activator Core Concepts 189 Designing and Deploying Solutions 194 No-Code Rule Authoring 194 Triggered Actions 200 Previews and Impact Estimation 202 Monitoring and Observability 203 Practical Use Cases 207 Conclusion 209 9. Kusto Query Language. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 211 Introduction to Kusto Query Language 212 Basic Query Building Blocks 214 Filtering and Shaping Data 215 Aggregating and Ordering Data 221 Advanced Queries 228 Aggregating by More than One Field 228 Visualizing Time-Based Data 229 Visualizing Categorical Distributions with Pie Charts 231 Using the Make-Series Operator to Generate a Time Series 233 Joins in KQL 234 Join Syntax 234 Types of Joins 234 Optimizing Joins 239 Update Policies 240 Best Practices and Performance Tips 241 Reducing Scanned Data with Efficient Filtering 242 Using Indexed Time Filtering with between and bin() 242 Using has, instead of contains, for String Searches 243 Using project to Limit Columns 244 Aggregating Data Early with summarize 245 Using Materialized Views for Frequently Queried Data 245 Avoiding order by Unless It’s Necessary 246 vi | Table of Contents

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Using let to Store Intermediate Results 247 Optimizing Regular Expressions 248 Conclusion 249 10. Real-Time Dashboards. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 251 Understanding Real-Time Dashboards 252 What Is a Real-Time Dashboard? 252 Real-Time Dashboards in Microsoft Fabric 253 Real-Time Dashboards Versus Power BI Reports 254 Live Data in Power BI 254 Comparison Between Real-Time Dashboards and Power BI Reports 255 When to Use Which 255 Using Real-Time Dashboards in Power BI Apps 259 Practical Example 260 Building a Heatmap 260 Comparing Two Time Periods 263 Cumulating Values 265 Identifying the Busiest Day 267 Adding a Title and an Image 268 Visualization Types for Real-Time Dashboards 271 Use Cases and Scenarios for Real-Time Dashboards 272 Best Practices for Designing Real-Time Dashboards 273 Common Pitfalls to Avoid 274 Design Patterns for Real-Time Dashboards 274 Conclusion 275 11. Administration of Your Real-Time Intelligence Environment. . . . . . . . . . . . . . . . . . . . . 277 Foundational Administration Concepts 278 Workspace Management 278 Central Administration 279 Tenant-Level Administration 280 Capacity and Resource Management 282 Understanding Fabric Capacity 282 Real-Time Intelligence Capacity Overview Events 283 Observability and Monitoring 287 Activity Monitoring 288 Monitoring Capacity Performance 289 Auditing 290 Alerting and Notifications 290 FinOps 290 Security Fundamentals 295 Security Architecture Overview 295 Table of Contents | vii

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Identity and Access Management 297 Data Security 299 Component-Level Security 301 Real-Time Dashboards 303 Activator (Reflex) 303 Conclusion 303 12. Automation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 305 Why Automate? 305 The Three Loops That Govern the Layers of Fabric Automation 306 Loop 1: Platform Baseline (Reproducible Environmental Structure) 307 Loop 2: Standardizing Lifecycle Pipelines 307 Loop 3: Deployment from Source Control (Code-First Promotion) 308 The Control Surface: REST API As the Foundation 308 REST API: The Foundational Layer 308 Authentication Boundary 309 The Universal Call Pattern 310 Where Direct REST Usage Becomes Necessary 311 Architectural Perspective 311 A Delivery Narrative: From the First Workspace to Steady Releases 312 Terraform for Fabric 312 What Terraform Does 313 Terraform Drift Versus Fabric Definitions 314 Operational Automation: The Day-Two Surface (MicrosoftFabricMgmt + Fabric CLI) 315 MicrosoftFabricMgmt As the Operator’s Console 315 Fabric CLI for Cross-Platform Ops and Lightweight Automation 316 Application Lifecycle Management: Git Integration, Deployment Pipelines, and fabric-cicd As the Definition Authority 318 An Alternative to Deployment Pipelines: fabric-cicd 319 Installing fabric-cicd 320 Choosing Between Deployment Pipelines and fabric-cicd 321 A Reference Automation Blueprint 322 Loop 1: Baseline Phase (Once per Environment) 322 Loop 2: Authoring and Change Phase (Continuous) 322 Loop 3: Publishing and Promotion Phase (Once per Release) 322 Drift and Confidence Controls (Ongoing) 323 Day-Two Operational Surface (Continuous) 323 Practical Automation Guidelines 323 Treat Fabric Artifacts As Production Infrastructure 323 Favor Idempotent Automation 324 Use the Right Tool for the Right Responsibility 325 viii | Table of Contents

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Secure and Isolate Automation Identities 326 Parameterize Environments Explicitly 326 Monitor What You Automate 327 Conclusion 328 What Comes Next? 329 Index. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 331 Table of Contents | ix

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Foreword We stand at the intersection of two of the most profound revolutions in modern history: the data and AI revolutions. Each is reshaping our world, and each is driving the other forward in a relentless, symbiotic loop of technological transformation. The first revolution is one of data, and it is a silent, relentless tsunami. We have moved far beyond gigabytes and terabytes; we now speak in petabytes and exabytes. But this revolution is not just about volume. It is defined by velocity and granu‐ larity—a constant, high-speed stream of information describing every click, every sensor reading, every transaction, and every interaction. The second revolution, which has burst into our collective consciousness with aston‐ ishing force, is that of AI. Generative AI, copilots, and large language models are not just incremental improvements. They represent a fundamental paradigm shift in how we interact with technology and, increasingly, how our businesses operate. I have had a front-row seat for the evolution of both data and AI for the past 25 years. My career in data began when the immediate challenge was simply capturing and storing information. We, as an industry, built massive, structured data warehouses to answer critical but retrospective questions. Business intelligence was the art of looking in the rearview mirror—compiling weekly sales reports, analyzing quarterly trends, and understanding what happened. But by the time a report landed on a desk, the data it described was already hours, days, or even weeks old. In that world, this was not only acceptable; it was a triumph of engineering. Then, I watched as the nature of data itself began to change. It became faster, more voluminous, and profoundly less structured. It was no longer just the transaction; it was the entire digital exhaust of the business. Simultaneously, AI evolved from a futuristic concept into a practical, powerful part‐ ner. It promised to be our analyst, our assistant, and our operational copilot—capable of synthesizing vast information and taking intelligent action in seconds. xi

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This created a jarring, fundamental disconnect: we have AI that can “think” and act in real time, but our data systems are still stuck in the past. An AI operating on stale data is an AI driving blind. It cannot act on a fraudulent transaction that happened yesterday, redirect a supply chain in response to a sudden event that occurred last night, or respond to a customer complaint that is already an hour old. This is the great digital impasse of our time. This chasm—between the moment data is created and the moment we can meaningfully act on it—is where we lose opportunities, customers lose trust in us, and digital services fail. For years, I, like many of my colleagues in the industry, tried to bridge this gap with the tools we had. We built fantastically complex, brittle pipelines, stitching together a dozen different services. We created specialized silos for our telemetry data, different silos for our streaming data, and still others for our core business data. The result was a fractured, fragile, and impossibly expensive architecture that only the largest, most elite data engineering teams could even attempt to manage. It was out of reach for almost everyone else. But from this complexity came our aha! moment and the inception of our vision. We realized we were trying to use existing tools to patch the symptoms of the problem, instead of creating a solution to address the root cause. We saw that the high-speed data from our application logs, the telemetry from our Internet of Things (IoT) devices, and the clickstreams from our websites were not intrinsically different. They were all part of the same class of data, and they all needed to be correlated, in the moment, with the core business data that provided context. This was a pervasive problem for both us and our customers. So we asked ourselves some key questions: • What if we stopped building silos and instead built one unified and integrated foundation? • What if the same platform that analyzed a website clickstream could also process a billion sensor readings and correlate it all—in real time—with customer order history from a central data lake? • What if we made it not a complex, bespoke engineering project but a simple, accessible, foundational software-as-a-service experience for every organization? This book is about that foundation—Fabric Real-Time Intelligence—and our vision coming to life with it. This book is not just a technical manual for a single product. It is a strategic guide to a new way of thinking and operating. The true power of this new approach is not just its speed but its seamless integration into the rest of the Fabric ecosystem, which creates a complete solution. xii | Foreword

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We are beyond building dashboards. Our goal is to build intelligent, automated, self- operating systems—ones that help create an organization that doesn’t just observe what is happening but can also be proactive and reactive to changing business needs, with the help of machines. Fabric Real-Time Intelligence is the foundation for truly AI-driven operations. The journey from data to insight to action is no longer a weeklong relay race; it is a continuous, instantaneous, and intelligent loop. This book is for the innovators—the data engineers, analysts, citizen developers, and business leaders—who look at the dual revolutions of data and AI and are not content to simply watch them pass by. It is a guide for the builders who are ready to harness them. Welcome to the new age of Real-Time Intelligence. — Yitzhak Kesselman CVP of Messaging and Real-Time Analytics, Microsoft Foreword | xiii

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Preface Today’s world is rich with incredibly granular data, which is generated from many different sources: IoT devices, social platforms, transaction systems, and real-time sensors. Every second, businesses, governments, and individuals generate huge amounts of hypergranular data, from customer service touches to machine perfor‐ mance metrics at subsecond resolutions. The sudden rise in the granularity of data has spawned tremendous opportunities for organizations to gain profound insights—and also monumental storage, process‐ ing, and analytical challenges. Organizations are faced with a dual task: keeping up with this deluge of data while simultaneously pulling actionable intelligence quickly enough to stay competitive. In this digital age, the pace of business requires prompt decision making with the assistance of timely insights. As Kelly Herrell wrote in Forbes in February 2024, “There is a rapidly growing set of use cases that need ‘real-time’ speeds, generating decisions and actions at least 20 times faster than the blink of an eye.” Enterprises need to base their decisions on real-time data to detect trends, react to changes, and project. In customer behavior monitoring, supply chain optimization, and security enhancement, the ability to analyze data in transit is rapidly giving companies a competitive edge. For years, we have been fascinated by the power of the Kusto Query Language (KQL) and Azure Data Explorer engine—the “hidden gem” of the Azure stack. Until now, harnessing this power required developers to piece together complex pipelines and connect disparate services by hand. But with the launch of Microsoft Fabric, this engine has been natively embedded into a unified platform called Real-Time Intelligence. In this book, we’ll explore how you can use Microsoft Fabric and Real-Time Intel‐ ligence to enable your organizations to make informed decisions that are based on up-to-the-minute data. By analyzing live information, businesses can respond immediately to changing conditions, optimize processes, detect anomalies, and gain a xv

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competitive edge. And by improving organizations’ abilities in critical areas like mon‐ itoring their supply chains, preventing fraud, and enhancing customer experiences, Fabric and Real-Time Intelligence can empower organizations to act decisively in the dynamic landscape of today’s data-driven world. Who Should Read This Book This book is for anyone who wants to really get a handle on real-time data with Microsoft Fabric—how to design it, run it, and make it actually useful. Who’s going to get the most value here? These groups come to mind: • Data engineers and architects who need to wrangle streams at scale and keep pipelines reliable • Business intelligence developers and analysts who are tired of static reports and want dashboards that actually move when the data does • IT and ops folks who live in the world of monitoring, alerts, and “keep the lights on” moments • Solution architects and consultants who design platforms and need to know where Real-Time Intelligence fits into the bigger data picture • Developers and DevOps engineers who want automation baked in—think APIs, CLIs, and continuous integration/continuous delivery (CI/CD) pipelines • Decision makers and leaders who are asking, “What could we do differently if we had insights right when things happen?” But even if you’re not in any of these groups, don’t panic—you don’t need to be a Fabric expert to follow along. If you know the basics of cloud data platforms or have worked with Power BI, you’ll feel right at home. And even if these concepts are brand-new to you, we’ll walk through them step by step. So whether you’re looking to spin up your very first streaming dashboard, upgrade a clunky batch system, or roll out real-time analytics across an entire enterprise—this book is here to help you get there. Why We Wrote This Book Not long ago, we were exactly where you might be right now: piecing together data pipelines, streaming events from IoT devices, wiring up Azure services by hand, and hoping our dashboards would refresh faster than “once a day at 2:00 a.m.” We also knew real-time data could work. We’d seen glimpses of it in Azure Data Explorer, custom event hub setups, and streaming architectures that took weeks (sometimes months) to stitch together. But it always felt like a puzzle with missing xvi | Preface

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pieces. Too many moving parts. Too many service limits to memorize. Too many unanswered questions. Was this really production ready? Was it worth the complexity and cost? Was anyone actually running this at scale or just in demos? That’s the frustration that pushed us to write this book. When Microsoft Fabric introduced Real-Time Intelligence, the lightbulb went on. Finally, ingestion, processing, querying, and visualization weren’t spread across half a dozen tools. They were all in one place. That was powerful, but it also meant that there was a lot to absorb. Without a guide, the learning curve would feel steep. The official docs are great at showing you what to click and how to run a query, but what they don’t always explain is the why. Why pick one option over another to help you piece things together into something that actually solves a business problem and isn’t just a “Hello, World!” demo? We kept wishing for a resource that was practical, honest, and grounded in real-world experience. Over the past few years, the two of us, Johan and Frank, have talked about Real-Time Intelligence pretty much everywhere the data community gathers: PASS Data Com‐ munity Summit, SQL Konferenz in Germany, SQLBits, FabCon and FabCon Europe, dataMinds Connect, local user group meetings, Microsoft Partner trainings…the list goes on. And every time, the conversations were eye-opening. We’d see what excited people, where the sticking points were, and which questions kept popping up, no matter the audience. Those hallway chats, the Q&As after sessions, even the follow-up emails all shaped how we thought about this topic. This book is our long-form answer to those moments when we wished we could have shared our insights, explanations, and lessons learned in more detail when the session clock ran out. We’ve also been fortunate to spend time with the Microsoft Fabric product teams— not just watching from the sidelines, but actually testing early features, tossing feed‐ back their way, and sometimes seeing things before they even had proper names. Those chats gave us a perspective we never could’ve gotten just tinkering on our own. As longtime Microsoft MVPs, we’ve always loved sharing what we know with the community. It’s kind of in our DNA at this point, and writing this book felt like the next logical step. It’s a way to pull together everything we’ve picked up—lessons from the field, insights from countless community conversations, and even behind- the-scenes chats with the product team—and turn them into something practical you can actually use every day. You’ll see pieces of those influences throughout this book. It’s not only our take as practitioners and trainers. It’s also colored by the people who are literally building Fabric Real-Time Intelligence behind the scenes. So it gives you a view from both worlds: the field and the folks writing the code. Preface | xvii

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So in the end, we decided to write the book we wish we’d had when we started. Our goals for it are pretty straightforward. We want this book to do these things for you: • Give you a map through the noise, so you don’t burn weeks on the basics. • Provide examples that go beyond theory: actual streaming dashboards, automa‐ ted ingestion, and solutions that scale. • Pass along the little lessons we’ve learned the hard way, the quirks and details that separate a prototype from a system you can trust. • And maybe most importantly, inspire you to flip your perspective: stop only looking backward at yesterday’s data and start asking, “What’s happening right now, and how do we act on it?” At the end of the day, Real-Time Intelligence isn’t just another button in Fabric. It’s a different way of working. It helps businesses react faster, keeps users better informed, and gives teams the confidence to make smarter calls in the moment. If this book helps you skip the trial-and-error phase, gives you a few aha moments, and leaves you confident that you can build real-time solutions in Fabric, then we will have done what we set out to do. Navigating This Book This book is organized into the following chapters: Chapter 1, “Introduction to Microsoft Fabric” This chapter introduces Fabric as Microsoft’s unified, end-to-end data and ana‐ lytics platform. Fabric’s integrated architecture simplifies multivendor complexity by combining data engineering, warehousing, science, real-time analytics, and visualization. You’ll learn about Fabric’s core workloads, its underlying OneLake foundation, the different data storage paradigms, and the shared metadata plat‐ form. This chapter also provides an overview of Fabric’s licensing, capacity, and workspace model. Chapter 2, “Business Intelligence Versus Real-Time Intelligence” This chapter clarifies the essential differences between business intelligence and real-time intelligence. You’ll learn what “real time” really means in practice. You’ll also compare core features, costs, and complexity, then ground them in concrete use cases—from sales and finance reporting to anomaly detection, predictive maintenance, and live operations. Finally, the chapter highlights how Fabric removes much of the traditional real-time intelligence friction and how integrating real-time dashboards with Power BI delivers the best of both worlds: instant signals with deep, contextual analysis in one experience. xviii | Preface