SQL Database in Microsoft Fabric: Simplified, Autonomous, and AI-Optimized

Microsoft Fabric unifies databases, data integration, analytics, real-time intelligence, and BI on one governed platform with OneLake storage. This session explains SQL database in Fabric, its three design pillars—simplified, autonomous and secure, and optimized for AI—and the scenarios customers use it for, such as operational data stores, event-driven apps, Power Apps, metadata-driven pipelines, and RAG applications.

■ Fabric and SQL database baseline
- One SQL approach from ground to cloud to Fabric; same SQL engine as SQL Server 2025 and Azure SQL
- Shared governance, security, and OneLake across Fabric workloads; Copilot built into the platform
- Common scenarios include operational data stores, event-driven apps, low-code Power Apps, reverse ETL, Power BI write-back, and reference data

■ Simplified provisioning and analytics
- Create a SQL database from the Fabric web interface in minutes with no server configuration
- T-SQL editing with IntelliSense and Copilot; unified Fabric capacity billing
- Automatic near-real-time replication to OneLake in Delta format and a SQL analytics endpoint

■ Migration and source control
- SQL Server Migration Tool with DACPAC validation and guided migration
- GitHub and Azure DevOps integration from the Fabric web interface
- SQL projects in VS Code and Visual Studio, plus SQL package and DACPAC deployment options

■ Autonomous, secure, and AI-optimized
- Serverless, auto-scaling, zone-redundant architecture with automatic backups, patching, and indexing
- Microsoft Entra authentication, auditing, customer-managed keys, and Microsoft Purview integration
- Vector data type, external models, embeddings, similarity search, and RAG patterns from T-SQL
- Integration with Power BI, notebooks, data agents, GraphQL, and Microsoft Foundry

Suited to developers, data professionals, and architects evaluating Fabric for operational and AI applications. It focuses on product capabilities and demonstrations rather than detailed pricing or benchmark comparisons. Viewers gain a practical view of database creation, migration, source control, security, and vector search workflows to assess for their own projects.