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Learn Microsoft Fabric

You're reading from  Learn Microsoft Fabric

Product type Book
Published in Feb 2024
Publisher Packt
ISBN-13 9781835082287
Pages 338 pages
Edition 1st Edition
Languages
Authors (2):
Arshad Ali Arshad Ali
Profile icon Arshad Ali
Bradley Schacht Bradley Schacht
Profile icon Bradley Schacht
View More author details

Table of Contents (19) Chapters

Preface 1. Part 1: An Introduction to Microsoft Fabric
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

Data engineering

The amount of data an organization needs to ingest and process at scale is growing faster than ever before, ranging from tabular data to unstructured documents, images, videos, IoT sensors, and more. Hence, the role of data engineering is increasingly becoming complex yet important in any organization’s analytics journey. Data engineering capabilities in Fabric allow you to enrich your data for higher quality and organize it in a way that is easily accessible to the right individuals with the right access and at the right time. These native data engineering capabilities allow data developers and data engineers to quickly and efficiently build data transformation flows that allow them to collect, store, process, and analyze large volumes of data by leveraging the power of open source Apache Spark (https://spark.apache.org/) to transform their data at scale and build out a robust lakehouse architecture. When you switch to the data engineering experience, you will...

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