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

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

18. Other Books You May Enjoy

End-to-end data science scenario

A typical data analytics system for data science in Fabric would consist of the components and layers shown in Figure 6.1:

Figure 6.1 – Reference architecture for data science in Fabric

Let’s review these components in detail:

Data sources: To ingest data into the lakehouse either from Azure data services or from other cloud platforms or on-premise sources, Fabric provides native or built-in ready-to-use connectors to make use of it, which makes building a data ingestion flow quick and easy. In Fabric, you might also use the data from the lakehouse and data warehouse, which you have brought in and transformed, to train your model.
Data cleansing and preparation: Fabric offers different options for you to prepare, clean, and transform your data before you train your model efficiently. For example, if you prefer a user interface experience, you can use Data Wrangler, with its intuitive interface...

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