Learn Microsoft Fabric : A Practical Guide to Performing Data Analytics in the Era of Artificial Intelligence / Arshad Ali and Bradley Schacht.

Format:

Book

Author/Creator:

ʻAlī, Arshad, author.

Schacht, Bradley, author.

Language:

English

Subjects (All):

Windows Azure.

Application software--Development.

Application software.

Software architecture.

Cloud computing.

Internet of things.

Computer network architectures.

Physical Description:

1 online resource (338 pages)

Edition:

First edition.

Place of Publication:

Birmingham, England : Packt Publishing, [2024]

Biography/History:

Ali Arshad: Arshad Ali is a principal product manager at Microsoft, working on the Microsoft Fabric product team in Redmond, WA. He focuses on Spark Runtime, which empowers both data engineering and data science experiences. In his previous role, he helped strategic customers and partners adopt Azure Synapse and Microsoft Fabric. Arshad has more than 20 years of industry experience and has been with Microsoft for over 16 years. He is the co-author of the book Big Data Analytics with Azure HDInsight and the author of over 200 technical articles and blogs on data and analytics. Arshad holds an MBA from the Foster School of Business at the University of Washington and an MCA from India. Schacht Bradley: Bradley Schacht is a principal program manager on the Microsoft Fabric product team based in Saint Augustine, Florida. Bradley is a former consultant and trainer and has co-authored five books on SQL Server and Power BI. As a member of the Microsoft Fabric product team, Bradley works directly with customers to solve some of their most complex data problems and helps shape the future of Microsoft Fabric. Bradley gives back to the community by speaking at events, such as the PASS Summit, SQL Saturday, Code Camp, and user groups across the country, including locally at the Jacksonville SQL Server User Group (JSSUG). He is a contributor on SQLServerCentral and blogs on his personal site, BradleySchacht.

Summary:

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, and the wider analytics landscape. In this book, you'll explore workloads such as Data Factory, Synapse Data Engineering, data science, data warehouse, real-time analytics, and Power BI. You’ll learn how to build end-to-end lakehouse and data warehouse solutions using the medallion architecture, unlock the real-time analytics, and implement machine learning and AI models. As you progress, you’ll build expertise in monitoring workloads and administering Fabric across tenants, capacities, and workspaces. The book also guides you step by step through enhancing security and governance practices in Microsoft Fabric and implementing CI/CD workflows with Azure DevOps or GitHub. Finally, you’ll discover the power of Copilot, an AI-driven assistant that accelerates your analytics journey. By the end of this book, you’ll have unlocked the full potential of AI-driven data analytics, gaining a comprehensive understanding of the analytics landscape and mastery over the essential concepts and principles of Microsoft Fabric.

Contents:

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

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

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

SQL endpoint

Orchestrate data ingestion and transformation flow and schedule notebooks and pipelines

Data meshes in Fabric - a primer

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 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

Chapter 5: Building an End-to-End Analytics System - Real-Time Analytics

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

Chapter 6: Building an End-to-End Analytics System - Data Science

End-to-end data science scenario

Data and storage - creating a lakehouse and ingesting data using Apache Spark

Problem formulation/ideation (business understanding)

Data acquisition, discovery, and preprocessing

Data acquisition

Data discovery

Data preprocessing

Experimenting and modeling

Training - version 1

Training - version 2

AutoML with FLAML

Enriching and operationalizing

Analyzing and getting insights

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

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)

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

Chapter 10: Continuous Integration and Continuous Deployment (CI/CD)

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

Part 5: AI Assistance with Copilot Integration

Chapter 11: Overview of AI Assistance and Copilot Integration

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

Index

Other Books You May Enjoy.

Notes:

Includes index.

Description based on publisher supplied metadata and other sources.

Description based on print version record.

ISBN:

9781835084342

1835084346

OCLC:

1424950850