Build AI Agents on Microsoft Fabric DWH
- No credit card required
- 5-minute setup
- SOC 2 Type II certified
Build AI Agents on top of Microsoft Fabric DWH
Build intelligent AI Agents on top of Microsoft Fabric DWH, with Peliqan’s AI data foundation:
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- Microsoft Fabric DWH MCP Server – let Claude, ChatGPT, or any AI client query and update your Microsoft Fabric DWH data
- Microsoft Fabric DWH in n8n AI Agents – build automated workflows that reason on your data
- Microsoft Fabric DWH in Make – trigger AI-powered actions from your Microsoft Fabric DWH events
- Text-to-SQL on Microsoft Fabric DWH – ask questions in plain English, get SQL-powered answers
- Microsoft Fabric DWH RAG – semantic search with out-of-the-box embeddings (vectors)
- Microsoft Fabric DWH Graph RAG – traverse relationships across your data entities
- Unified 360° data – combine Microsoft Fabric DWH with 250+ other sources in one warehouse
Microsoft Fabric DWH MCP Server
Publish a Microsoft Fabric DWH MCP Server with full read and write capabilities.
Peliqan syncs a read-only copy of your Microsoft Fabric DWH data into its built-in warehouse – your AI agents query the cache, your live Microsoft Fabric DWH is never touched. Connect Claude, ChatGPT, or any MCP client and start querying Microsoft Fabric DWH in plain English.
Microsoft Fabric DWH in n8n
Build AI Chatbots and AI Agents in n8n that query your Microsoft Fabric DWH data using Text-to-SQL and perform RAG on Microsoft Fabric DWH documents and records.
Peliqan acts as the data layer – syncing your Microsoft Fabric DWH data, converting natural language to SQL, and returning results your n8n agent can act on.
Implement a “Text to SQL” chatbot on Microsoft Fabric DWH
Implement an AI Chatbot that answers analytical questions on Microsoft Fabric DWH data using Text-to-SQL.
Ask “what were my top 10 customers last quarter?” or “which deals are stalled?” and get instant, accurate answers.
Peliqan converts your question to SQL, runs it against your Microsoft Fabric DWH warehouse, and returns structured results – no SQL knowledge required.
Implement a chatbot with RAG on Microsoft Fabric DWH
Implement RAG (retrieval-augmented generation) on Microsoft Fabric DWH data with Peliqan’s out-of-the-box vector store.
Peliqan automatically creates embeddings of your Microsoft Fabric DWH records, notes, and documents – so your AI agents can search semantically, not just by keywords.
Combine RAG with Text-to-SQL for AI that can both reason on numbers and understand context.
Combine Microsoft Fabric DWH data with 250+ sources
Combine data from Microsoft Fabric DWH with 250+ other connectors in Peliqan’s built-in warehouse.
Build unified 360° views of customers, deals, products, or employees – pulling from Microsoft Fabric DWH, your CRM, ERP, HR systems, and more.
Feed these unified data models to your AI Agents so they can answer cross-functional questions in a single query.
Prepare your Microsoft Fabric DWH data for AI
Access, combine, and report on data from Microsoft Fabric DWH and all your SaaS apps instantly. Gain valuable insights by bringing all your business data together in one place within minutes.
Unify, Automate & Activate Your Data
Connect all your SaaS apps, databases, and spreadsheets into one workspace. Build automations, analytics pipelines, and data apps — all in one place.
Frequently Asked Questions
Why do I need Peliqan for my AI ?
Most AI tools can’t access your Microsoft Fabric DWH data directly. Peliqan solves this by syncing Microsoft Fabric DWH into a built-in data warehouse and exposing it through MCP, Text-to-SQL, and RAG.
Your AI agents get governed, real-time access to Microsoft Fabric DWH data combined with 250+ other sources – without building custom integrations.
Peliqan is the only platform that lets AI agents combine structured SQL queries with semantic RAG search on Microsoft Fabric DWH data in a single context.
How to build an AI agent for Microsoft Fabric DWH ?
There are different ways to build an AI agent that can query data in Microsoft Fabric DWH and take actions in Microsoft Fabric DWH. For example you can build an AI agent in n8n and use Peliqan as the data foundation. Peliqan will sync your Microsoft Fabric DWH data to its built-in data warehouse and allow the AI Agent to perform “Text to SQL” and RAG to answer questions and to perform reasoning on Microsoft Fabric DWH data, combined with data from 250+ other sources.
How can I use Microsoft Fabric DWH in n8n ?
First sign up for a free trial on Peliqan.io, next connect Microsoft Fabric DWH in Peliqan. Once that is done, create an AI agent in n8n and use the Peliqan n8n node in your worflow. Add Peliqan as a “tool” to your AI Agent node, so that the AI agent can query your Microsoft Fabric DWH data using Text to SQL.
How to use RAG with Microsoft Fabric DWH ?
There are different options to use RAG (retrieval augmented generation) in your AI Agent with Microsoft Fabric DWH data. One option is to create a workflow in n8n that fetches all Microsoft Fabric DWH data from Peliqan and stores it in Supabase as a vector store, with embeddings created using e.g. OpenAI.
How to build an MCP Server for Microsoft Fabric DWH ?
Connect Microsoft Fabric DWH in Peliqan, then create an API handler using the built-in MCP template. Expose the Microsoft Fabric DWH tables and actions your AI needs, set role-based permissions, and run pip install mcp-server-peliqan to connect Claude or ChatGPT. Your AI agents query a cached copy of your data – your live Microsoft Fabric DWH is never touched. The full setup takes under 10 minutes.
How do I build an AI chatbot with “Text to SQL” for Microsoft Fabric DWH ?
n8n is a great tool to build AI chatbots that use Text to SQL, to answer any analytical question on your Microsoft Fabric DWH data. Any question will be converted by the AI agent into an SQL query, which is executed by Peliqan on the Microsoft Fabric DWH data in the data warehouse.
How to create embeddings in a vector store for Microsoft Fabric DWH data ?
In order to prepare your Microsoft Fabric DWH data for RAG, you need to create embeddings and store them in a vector store. This can be done by creating a workflow in n8n that fetches all Microsoft Fabric DWH data from Peliqan and stores it in Supabase as a vector store, with embeddings created using e.g. OpenAI.
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