You're reading from Learn Microsoft Fabric A practical guide to performing data analytics in the era of artificial intelligence

Product type Paperback

Published in Feb 2024

Publisher Packt

ISBN-13 9781835082287

Length 338 pages

Edition 1st Edition

Tools

Azure Data Factory

Concepts

Artificial Intelligence

Authors (2):

Bradley Schacht

Arshad Ali

View More author details

Table of Contents (19) Chapters

Preface

1. Part 1: An Introduction to Microsoft Fabric FREE CHAPTER

2. Chapter 1: Overview of Microsoft Fabric and Understanding Its Different Concepts

3. Chapter 2: Understanding Different Workloads and Getting Started with Microsoft Fabric

4. Part 2: Building End-to-End Analytics Systems

5. Chapter 3: Building an End-to-End Analytics System – Lakehouse

6. Chapter 4: Building an End-to-End Analytics System – Data Warehouse

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

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

9. Part 3: Administration and Monitoring

10. Chapter 7: Monitoring Overview and Monitoring Different Workloads

11. Chapter 8: Administering Fabric

12. Part 4: Security and Developer Experience

13. Chapter 9: Security and Governance Overview

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

15. Part 5: AI Assistance with Copilot Integration

16. Chapter 11: Overview of AI Assistance and Copilot Integration

17. Index

Why subscribe?

18. Other Books You May Enjoy

Data Warehouse

Businesses rely on a single version of truth to operate effectively and the tool of choice for data-driven organizations around the globe has long been the data warehouse. Names such as Ralph Kimball and Bill Inmon are synonymous with design patterns, and Kimball’s The Data Warehouse Toolkit could be found on the desk of just about any BI developer in the 2010s. While the technology has changed and the lakehouse is becoming more popular, the value of a data warehouse remains and companies are embarking on new warehouse projects every day.

The decision between a data warehouse and a lakehouse in Fabric is largely one of skillset rather than technology. Data Factory ETL tooling, Power BI datasets, SQL, and Spark all work seamlessly over both lakehouses and warehouses, and they both deliver massive scale and high performance. The primary difference comes down to the skillset: Spark for lakehouses versus T-SQL for data warehouses.

If companies have developer...