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Learn Microsoft Fabric

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

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Product type Paperback
Published in Feb 2024
Publisher Packt
ISBN-13 9781835082287
Length 338 pages
Edition 1st Edition
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Authors (2):
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Bradley Schacht Bradley Schacht
Author Profile Icon Bradley Schacht
Bradley Schacht
Arshad Ali Arshad Ali
Author Profile Icon Arshad Ali
Arshad Ali
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Table of Contents (19) Chapters Close

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

Data transformation using T-SQL

With the data now loaded, it is time to transform the data into an aggregate reporting table. In this section, you will create and execute a stored procedure that will create an aggregate table that will be used later in this chapter as the base for a Power BI report.

Let’s look at transforming data using T-SQL:

  1. While still in the query editor of the WideWorldImportersDW data warehouse, click on New SQL query from the ribbon.
  2. In the query window, enter the following code:

Note

The following code should not be used as an example of best practices. In a real-world scenario, loading a data warehouse will involve generating a surrogate key (warehouse key), updating dimension data based on type 1 and type 2 attributes, handling NULL dimension attributes, looking up surrogate key values when loading a fact table, and more. The following code represents a simplified approach to loading dimension and fact tables to show the general...

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