Learn Microsoft Fabric A Practical Guide to Performing Data Analytics in the Era of Artificial Intelligence
Discover the capabilities of Microsoft Fabric, the premier unified solution designed for the AI era, seamlessly combining data integration, OneLake, transformation, visualization, universal security, and a unified business model. This book provides an overview of Microsoft Fabric, its components, an...
| Otros Autores: | , |
|---|---|
| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Birmingham, England :
Packt Publishing
[2024]
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| Edición: | First edition |
| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009805123406719 |
Tabla de Contenidos:
- Cover
- Title Page
- Copyright and Credits
- Contributors
- Table of Contents
- Preface
- Part 1: An Introduction to Microsoft Fabric
- Chapter 1: Overview of Microsoft Fabric and Understanding Its Different Concepts
- Introduction to Microsoft Fabric
- Reviewing the core capabilities of Microsoft Fabric
- Complete analytics platform
- Lake-centric and open
- Empower every business user
- AI powered
- Unified business model with universal compute capacity
- Summary
- Chapter 2: Understanding Different Workloads and Getting Started with Microsoft Fabric
- Getting started with Microsoft Fabric
- Enabling Microsoft Fabric
- Checking your access to Microsoft Fabric
- Creating your first Fabric-enabled workspace
- Data Factory
- Pipelines
- Activities
- Connections
- Dataflow Gen2
- Loading data
- Data engineering
- Lakehouse
- Spark Job Definition
- Data Warehouse
- Simplifying the Data Warehouse experience
- Open and lake-centric
- Combining the lakehouse and data warehouse
- Loading data
- Querying the warehouse
- Data Science
- SynapseML
- MLflow integration
- FLAML integration for automated ML (AutoML)
- Data Wrangler
- Semantic Link
- Real-Time Analytics
- Eventstreams
- KQL databases
- KQL queryset
- Power BI
- Reports
- Datasets
- Direct Lake
- Summary
- Part 2: Building End-to-End Analytics Systems
- Chapter 3: Building an End-to-End Analytics System - Lakehouse
- Technical requirements
- Understanding end-to-end scenarios
- Understanding the end-to-end architecture
- Understanding sample data and data models
- Understanding data and transformation flow
- Storage
- Ingestion
- Transformation
- Importing notebooks
- Creating a shortcut (for Files): Silver zone
- Opening notebook and executing commands (loading to the Silver zone)
- Incremental data load.
- Creating a shortcut (for Tables): Gold zone
- Creating business aggregates for the Gold zone
- Analyze
- Power BI
- SQL endpoint
- Orchestrate data ingestion and transformation flow and schedule notebooks and pipelines
- Data meshes in Fabric - a primer
- Summary
- Chapter 4: Building an End-to-End Analytics System - Data Warehouse
- Understanding the end-to-end scenario
- Data and transformation flow
- Creating a data warehouse
- Creating tables in a data warehouse
- Loading data
- Loading data using the copy activity in Data Factory
- Loading data using T-SQL
- Data transformation using T-SQL
- Orchestrating ETL operations with Data Factory pipelines
- Analyzing data with Power BI
- Summary
- Chapter 5: Building an End-to-End Analytics System - Real-Time Analytics
- Understanding the end-to-end scenario
- Creating a Kusto Query Language (KQL) database
- Capturing and delivering data using eventstreams
- Analyzing data with KQL
- Reporting with Power BI
- Creating a new Power BI report
- Adding visualizations to the Power BI report
- Configure page refresh on the Power BI report
- Summary
- Chapter 6: Building an End-to-End Analytics System - Data Science
- Technical requirements
- End-to-end data science scenario
- Data and storage - creating a lakehouse and ingesting data using Apache Spark
- Importing notebooks
- Problem formulation/ideation (business understanding)
- Semantic Link
- Data acquisition, discovery, and preprocessing
- Data acquisition
- Data discovery
- Data preprocessing
- Data Wrangler
- Experimenting and modeling
- Training - version 1
- Training - version 2
- AutoML with FLAML
- Enriching and operationalizing
- Analyzing and getting insights
- Summary
- Part 3: Administration and Monitoring
- Chapter 7: Monitoring Overview and Monitoring Different Workloads.
- Technical requirements
- Overview of monitoring capabilities in Fabric
- Monitoring Data Factory pipelines and dataflows
- Monitoring Spark jobs (data engineering and data science)
- Monitoring data warehouse activity
- Monitoring Real-Time Analytics activity
- Monitoring eventstreams
- Monitoring KQL databases
- Monitoring capacity usage with the Microsoft Fabric Capacity Metrics app
- Summary
- Chapter 8: Administering Fabric
- Enabling Microsoft Fabric in your tenant
- What are capacities?
- Managing Fabric capacities
- Managing Spark job configurations
- Starter pools
- Custom Spark pools
- Spark runtime
- High concurrency
- Automatically tracking machine learning experiments and models
- Spark properties/configuration
- Library management
- Auto-tune
- Spark utility (MSSparkUtils)
- Summary
- Part 4: Security and Developer Experience
- Chapter 9: Security and Governance Overview
- Securing the Microsoft Fabric platform
- Guest users
- Conditional access
- Securing Microsoft Fabric workspaces and items
- Workspace-level permissions
- Item-level permissions
- Understanding governance and compliance in Microsoft Fabric
- Domains
- Microsoft Purview
- Summary
- Chapter 10: Continuous Integration and Continuous Deployment (CI/CD)
- Technical requirements
- Understanding the end-to-end flow
- Connecting to a Git repo with Azure DevOps
- Working on a new feature or release
- Creating and executing a deployment pipeline
- Managing database code for a Fabric data warehouse
- Managing database code with the SQL Database Projects extension
- Summary
- Part 5: AI Assistance with Copilot Integration
- Chapter 11: Overview of AI Assistance and Copilot Integration
- Technical requirements
- What is Copilot in Fabric?
- Copilot in data engineering and data science
- Copilot in Data Factory.
- Copilot in Power BI
- Creating reports with the Power BI Copilot
- Creating a narrative using Copilot
- Generating synonyms with Copilot
- Summary
- Index
- Other Books You May Enjoy.
