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SQL Databases in Microsoft Fabric

SQL Databases in Microsoft Fabric

31mIntermediate2025-05-13

Authors

Ben Weissman

Ben Weissman

Course details

This course introduces SQL Database in Microsoft Fabric, a developer-friendly transactional database service built on Azure SQL Database. Instructor BenWeissman explores how this service simplifies the creation and management of operational databases within the Fabric ecosystem. Learn about its seamless integration with various development frameworks and analytics tools, providing a unified platform for handling both transactional and analytical workloads. Benhighlights key features, such as automatic data replication into OneLake in near real-time, enabling analytics-ready data for downstream scenarios like data engineering and data science. Plus, learn about intelligent performance enhancements, including automatic index creation with Automatic Tuning, to boost database efficiency. Join Benin this course to understand how this service offers an end-to-end, easy-to-use solution.

Learning objectives
Explain the purpose of Databases in Microsoft Fabric.
Create a new Database in Fabric.
Differentiate on when to use traditional Fabric artifacts like a Warehouse or Lakehouse over using a SQL Database for your workloads.

Skills covered

Microsoft FabricBusiness AnalysisSQLDatabase AdministrationDatabase DevelopmentDatabase ManagementData AnalysisProject ManagementProgramming LanguagesData ScienceBusiness Analysis and StrategyBusiness Software and ToolsOpen SourceMicrosoftSoftware DevelopmentOne-Off

Concepts

0. Introduction

  • 01 - SQL databases
  • 02 - What you should know

1. What Is SQL Database in Microsoft Fabric

  • 03 - What is Microsoft Fabric
  • 04 - SQL database - The big picture
  • 05 - How does a SQL database in Fabric compare with Azure SQL Database
  • 06 - How is SQL database in Fabric different from Lakehouse or a Warehouse
  • 07 - Pricing

2. Working with SQL Databases in Microsoft Fabric

  • 08 - Setting up a new SQL database
  • 09 - Mirroring
  • 10 - Performance monitoring
  • 11 - Security
  • 12 - Integrated AI
  • 13 - GraphQL

Conclusion

  • 14 - Review

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