Feature Request: Azure SQL Connector and Fabric Data Warehouse Connector

[jlong1014]

Data Formulator enables users to load structured data from a range of sources and explore it with AI-assisted visualizations and analysis. Currently it provides data loaders for file formats (CSV/Parquet), cloud storage, and select databases. It would be highly valuable to extend this by adding native connectors for:

Azure SQL (Azure SQL Database & Managed Instance)

Microsoft Fabric Lakehouse

These connectors would allow users to seamlessly connect, query, and explore relational and Lakehouse data directly inside Data Formulator without manual exports or intermediate steps.

Use Cases:

• Enterprise SQL Workflows — Analysts working with enterprise data in Azure SQL could load tables, views, and query results directly into Data Formulator to visualize and explore trends.
• Lakehouse Analytics — Users with data in Microsoft Fabric Lakehouse (Delta Lake tables, SQL endpoints) could analyze large analytical datasets without needing external ETL tooling. Fabric is increasingly adopted for unified analytics and lakehouse scenarios, making a direct connector a strong enhancement.

Suggested Features:

Azure SQL Connector

• Support connection via connection string/credentials (with Entra Authentication, SQL authentication)
• Ability to browse databases, tables, views, and select/query data
• Option to load either full tables or filtered query results
• Ensure secure token handling and config persistence

Fabric Lakehouse Connector

• Support authenticated connection to Microsoft Fabric workspaces
• Browse Fabric Lakehouse tables and optionally SQL analytics endpoints/queries
• Load data from Delta Lake tables or SQL query results
• Support partitioned and large datasets efficiently

Benefits:

1. Reduces friction for enterprise users working with typical cloud data stores
2. Encourages adoption in Microsoft’s ecosystem by aligning with Azure and Fabric service patterns
3. Improves Data Formulator’s applicability to real world analysis scenarios

Technical Notes / Considerations:

For Azure SQL, leverage existing Python drivers (e.g., pyodbc, mssql-tools, pymssql/SQLAlchemy)

For Fabric Lakehouse, connect via REST APIs or Spark/ODBC interfaces, or utilize Fabric SQL endpoints where possible. Microsoft documentation already provides guidance on setting up Lakehouse connectors in Fabric Data Factory — this could guide how Data Formulator’s connector should authenticate and query data.
Microsoft Learn

Request:
Would the maintainers consider adding Azure SQL and Microsoft Fabric Lakehouse connectors?