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

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

Autorzy:: Johan Ludvig Brattĺs, Frank Geisler
Wydawnictwo:: O'Reilly Media (Z chęcią przeczytam książkę w języku polskim)
Ocena:: Bądź pierwszym, który oceni tę książkę
Stron:: 370
Dostępne formaty:: ePub

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In today's hyper-connected 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 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 AI-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

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Szczegóły książki

ISBN Ebooka:: 978-10-981-7854-3, 9781098178543
Data wydania ebooka :: 2026-05-07 Data wydania ebooka często jest dniem wprowadzenia tytułu do sprzedaży i może nie być równoznaczna z datą wydania książki papierowej. Dodatkowe informacje możesz znaleźć w darmowym fragmencie. Jeśli masz wątpliwości skontaktuj się z nami sklep@ebookpoint.pl.
Język publikacji:: angielski
Rozmiar pliku ePub:: 15MB
Rozmiar pliku Mobi:: 115.9MB

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Informatyka » Sztuczna inteligencja

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