Real-Time Intelligence with Microsoft Fabric. Empowering Data-Driven Decisions in the Era of AI Johan Ludvig Brattĺs, Frank Geisler

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Spis treści książki

• Foreword
• Preface
  • Who Should Read This Book
  • Why We Wrote This Book
  • Navigating This Book
  • Conventions Used in This Book
  • Using Code Examples
  • OReilly Online Learning
  • How to Contact Us
  • Acknowledgments
    • From Johan
    • From Frank
• 1. Introduction to Microsoft Fabric
  • Challenges of a Multiproduct Data Analytics Platform
  • Fabric Key Workloads and Capabilities
  • High-Level Architectural Overview
    • Common Data Store
    • Compute Engines
    • Common Metadata Platform
    • REST APIs
    • Security and Governance
  • Data Storage Paradigms
    • Data Warehouses
    • Data Lakehouses
    • Eventhouses
    • Choosing the Right Solution for Your Needs
  • Licensing and Capacities
    • Tenant
    • Capacity
    • Workspace
  • Conclusion
• 2. Business Intelligence Versus Real-Time Intelligence
  • Real Time Versus Near-Real Time
  • Business Intelligence
    • Key Features of Business Intelligence
    • Business Intelligence Use Cases
  • Real-Time Intelligence
    • Key Features of Real-Time Intelligence
    • Real-Time Intelligence Use Cases
      • Real-time monitoring and alerts
      • Predictive maintenance
      • Fraud detection and security
      • Personalized customer experience
      • Operational optimization
      • Health care applications
  • Key Differences Between Business Intelligence and Real-Time Intelligence
  • Selecting an Approach
  • Integrating Business Intelligence and Real-Time Intelligence
  • Conclusion
• 3. Real-Time Intelligence in Microsoft Fabric
  • The Components of Real-Time Intelligence
    • The Real-Time Hub
    • Eventhouses
    • KQL Databases
    • KQL Querysets
    • Eventstreams
    • Real-Time Dashboard
    • Activator
  • Conclusion
• 4. A Hands-on Project
  • The Scenario
    • The Coffee Machine Scenario
    • The Architecture of the Solution
    • Building the Medallion Architecture
    • The Data Schema
  • Getting Started
    • Prerequisites
    • Creating a Fabric Workspace
    • Creating a New Eventhouse
    • Enabling OneLake Availability
  • Ingesting and Transforming Data
    • Creating a New Eventstream
    • Importing the Coffee Cast Notebook
    • Running the Coffee Cast Notebook
    • Defining the Eventstream Topology
    • Enriching Data
      • Setting up the lakehouse
      • Uploading reference data files and creating Delta tables in the lakehouse
      • Accessing eventhouse data from the lakehouse via a shortcut
    • Building a KQL Database Schema for a Medallion Architecture
  • Adding Visualizations and Alerts
    • Building a Real-Time Dashboard
    • Using Data Activator for Alerts
  • Conclusion
• 5. Fabrics Eventstream Service
  • Key Challenges in Stream Processing
    • Time-Related Complexity
    • State Management
    • Scalability and Fault Tolerance
  • Stream-Processing Methodologies
  • Eventstream Overview
    • Sources and Destinations
    • Dataflow Model
    • Stream Processor
  • Data Transformation and Processing
    • Simple Transformations
      • Filtering and routing
      • Schema manipulation
      • Data type conversions
    • Advanced Transformations
      • Join operations
      • Union
      • Aggregations and windowing
        • Tumbling windows
        • Hopping windows
        • Sliding windows
        • Session windows
        • Snapshot windows
      • Custom transformations
  • A Hands-on Example
    • The Scenario
    • Getting Started
      • Creating an eventhouse and KQL databases
      • Creating the eventstream and adding source data
    • Processing the Data
      • Managing fields and adding a timestamp
      • Saving the raw data
      • Adding a GroupBy transformation
    • Creating a Real-Time Alert with Activator
    • Bonus Exercise: The SQL Option
  • Conclusion
• 6. Fabrics Eventhouse Service
  • Evolution and Purpose of Eventhouses and KQL Databases
  • Key Features of Eventhouse
    • Ribbon Menu
      • Enabling Always-on
      • Configuring minimum consumption
    • Explorer Pane
      • Viewing a system overview
      • Viewing databases
      • Monitoring the view
  • Key Features of KQL Databases
    • Core Architecture and Data Structure
    • Data Ingestion
    • Data Storage and Caching
    • Query Automation
      • Views
      • Materialized views
      • Stored functions
    • Data Integration and External Sources
      • Connecting to external data sources
        • External tables
        • OneLake shortcuts
      • Views and read/write operations on external data
    • Data Sharing and Replication
      • Shortcut/follower databases (read-only replicas)
      • OneLake sync
  • Anomaly Detection
  • Conclusion
• 7. The Real-Time Hub
  • Real-Time Hub Navigation
    • Streaming Data
      • Recent streaming data listings
      • Filters
      • Search
      • Actions
    • Stream Details
  • Adding Data
    • Data Source Listing
    • Filters
    • Actions
  • Subscribe to
  • Event Schema Registry
  • Conclusion
• 8. Activator
  • Introduction to Activator
    • Architectural Overview
    • Integration with the Fabric Ecosystem
    • Activator Core Concepts
      • Event-driven intelligence
      • Objects and object keys
      • Rules and state transitions
        • Key stateful operators
        • Preventing alert fatigue
  • Designing and Deploying Solutions
    • No-Code Rule Authoring
      • Intuitive UI and accessibility
      • Defining rules
      • Reusable logic
    • Triggered Actions
      • Types of actions
      • Event-to-action lifecycle
      • Asynchronous execution and custom payloads
    • Previews and Impact Estimation
    • Monitoring and Observability
      • Built-in monitoring capabilities in Fabric Activator
      • Runtime insights and analytics
      • Debugging with logs and diagnostics for root cause analysis
      • Ensuring reliability through observability
  • Practical Use Cases
  • Conclusion
• 9. Kusto Query Language
  • Introduction to Kusto Query Language
    • Basic Query Building Blocks
      • Retrieving sample data
      • Counting rows in a table
    • Filtering and Shaping Data
      • Filtering data based on conditions
      • Filtering data within a time range
      • Selecting specific columns
      • Filtering with multiple conditions
      • Searching for a substring in text columns
      • Finding strings that start with a certain letter
      • Renaming columns
    • Aggregating and Ordering Data
      • Grouping and summarizing data
      • Using aggregation functions
      • Grouping data into time intervals
      • Sorting data with the order by function
      • Getting the top N rows with the top function
      • Counting unique values
  • Advanced Queries
    • Aggregating by More than One Field
    • Visualizing Time-Based Data
    • Visualizing Categorical Distributions with Pie Charts
    • Using the Make-Series Operator to Generate a Time Series
  • Joins in KQL
    • Join Syntax
    • Types of Joins
    • Optimizing Joins
      • Distributing the join load among multiple compute nodes
      • Using the broadcast approach
      • Improving efficiency by projecting only the necessary columns
  • Update Policies
  • Best Practices and Performance Tips
    • Reducing Scanned Data with Efficient Filtering
    • Using Indexed Time Filtering with between and bin()
    • Using has, instead of contains, for String Searches
    • Using project to Limit Columns
    • Aggregating Data Early with summarize
    • Using Materialized Views for Frequently Queried Data
    • Avoiding order by Unless Its Necessary
    • Using let to Store Intermediate Results
    • Optimizing Regular Expressions
  • Conclusion
• 10. Real-Time Dashboards
  • Understanding Real-Time Dashboards
    • What Is a Real-Time Dashboard?
    • Real-Time Dashboards in Microsoft Fabric
  • Real-Time Dashboards Versus Power BI Reports
    • Live Data in Power BI
    • Comparison Between Real-Time Dashboards and Power BI Reports
    • When to Use Which
      • Real-Time Dashboards: For instant awareness
      • Power BI reports: For deep dives
      • Using Real-Time Dashboards and Power BI reports: Blurring lines in Microsoft Fabric
      • Guiding principles for choosing which to use
        • When to use Real-Time Dashboards
        • When to use Power BI reports
        • When to use them both together
    • Using Real-Time Dashboards in Power BI Apps
  • Practical Example
    • Building a Heatmap
    • Comparing Two Time Periods
    • Cumulating Values
    • Identifying the Busiest Day
    • Adding a Title and an Image
  • Visualization Types for Real-Time Dashboards
  • Use Cases and Scenarios for Real-Time Dashboards
  • Best Practices for Designing Real-Time Dashboards
  • Common Pitfalls to Avoid
  • Design Patterns for Real-Time Dashboards
  • Conclusion
• 11. Administration of Your Real-Time Intelligence Environment
  • Foundational Administration Concepts
    • Workspace Management
    • Central Administration
    • Tenant-Level Administration
  • Capacity and Resource Management
    • Understanding Fabric Capacity
    • Real-Time Intelligence Capacity Overview Events
      • Eventstreams
      • Eventhouses
      • Real-Time Dashboards
      • Activator
  • Observability and Monitoring
    • Activity Monitoring
    • Monitoring Capacity Performance
    • Auditing
    • Alerting and Notifications
  • FinOps
  • Security Fundamentals
    • Security Architecture Overview
    • Identity and Access Management
      • Microsoft Entra ID integration
      • Authentication mechanisms
      • Role-based access control
      • Service principals and managed identities
    • Data Security
      • Encryption at rest and in transit
      • OneLake security considerations for Real-Time Intelligence data
      • Data residency and compliance
    • Component-Level Security
      • Eventstreams
      • KQL databases
    • Real-Time Dashboards
    • Activator (Reflex)
  • Conclusion
• 12. Automation
  • Why Automate?
  • The Three Loops That Govern the Layers of Fabric Automation
    • Loop 1: Platform Baseline (Reproducible Environmental Structure)
    • Loop 2: Standardizing Lifecycle Pipelines
    • Loop 3: Deployment from Source Control (Code-First Promotion)
  • The Control Surface: REST API As the Foundation
    • REST API: The Foundational Layer
    • Authentication Boundary
    • The Universal Call Pattern
    • Where Direct REST Usage Becomes Necessary
    • Architectural Perspective
  • A Delivery Narrative: From the First Workspace to Steady Releases
  • Terraform for Fabric
    • What Terraform Does
    • Terraform Drift Versus Fabric Definitions
  • Operational Automation: The Day-Two Surface (MicrosoftFabricMgmt + Fabric CLI)
    • MicrosoftFabricMgmt As the Operators Console
      • Installing and exploring cmdlets
      • A common operational script approach
    • Fabric CLI for Cross-Platform Ops and Lightweight Automation
      • Fabric CLIs role in the automation model
      • Installation and authentication
      • Operational usage patterns
      • Strengths and boundaries
  • Application Lifecycle Management: Git Integration, Deployment Pipelines, and fabric-cicd As the Definition Authority
    • An Alternative to Deployment Pipelines: fabric-cicd
    • Installing fabric-cicd
    • Choosing Between Deployment Pipelines and fabric-cicd
  • A Reference Automation Blueprint
    • Loop 1: Baseline Phase (Once per Environment)
    • Loop 2: Authoring and Change Phase (Continuous)
    • Loop 3: Publishing and Promotion Phase (Once per Release)
    • Drift and Confidence Controls (Ongoing)
    • Day-Two Operational Surface (Continuous)
  • Practical Automation Guidelines
    • Treat Fabric Artifacts As Production Infrastructure
    • Favor Idempotent Automation
    • Use the Right Tool for the Right Responsibility
    • Secure and Isolate Automation Identities
    • Parameterize Environments Explicitly
    • Monitor What You Automate
  • Conclusion
  • What Comes Next?
• Index

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