# Vector Search in MS Fabric: e6data Powers Unified SQL + Semantic Search at 60% lower cost

Product · Apr 16, 2025 · e6data Team

> e6data brings SQL and AI together, letting you query unstructured data directly in Microsoft Fabric 10x faster and 60% lower cost—no pipelines, no workarounds, just fast answers.

Microsoft Fabric’s OneLake unified storage is a solid foundation. As of 2025, OneLake is a single, unified, logical data lake for your whole organization. Microsoft calls it: [OneDrive for data](https://learn.microsoft.com/en-us/fabric/onelake/onelake-overview). It brings structured and unstructured data under one roof. But some teams still struggle to query across formats without jumping through hoops:

![Vector search without e6data](/images/blog/ahOPc4gYeXexcqF4N0YvRC2XMCM.jpg)

*Vector search without e6data*

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-   Want to search across call transcripts, reviews, and dashboards? You need SQL *and* vector search (aka similarity or semantic search).  
      
    
-   Want relevance over exact match? You’re duct-taping keyword search, embedding databases, vector database alternatives, and ETL pipelines.  
      
    
-   Want speed at scale? You’re either bottlenecked by capacity units or drowning in cost from over-provisioning.

So most teams choose: either stay in SQL and miss out on meaning, or ship data out and break governance. Neither works long-term.

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## **Now, You Have e6data to Run Vector Search on Fabric**

![Now, You Have e6data to Run Vector Search on Fabric](/images/blog/bg5wN5fRHCBGWCnkCKHZtoqG6JI.jpg)

**e6data is a lakehouse compute engine which is** [**now integrated with Fabric**](https://www.e6data.com/blog/e6data-fabric-increased-performance-optimized-capacity)**. It brings fast, unified querying—structured** ***and*** **unstructured, in the same SQL statement.**

-   **10x performance**: Atomic scaling, coordinator-free architecture, [60% lower cost](https://www.e6data.com/blog/e6data-microsoft-fabric-faster-queries-lower-bills). Handles 1000+ QPS with sub-second latencies. A clear leap in vector similarity search performance.
-   **Semantic search built-in**: Turn reviews, tickets, and notes into embeddings for optimized semantic search and improved vector similarity search performance. Search by meaning, not just keywords
-   **No data movement**: e6data reads from OneLake directly. No duplication**‍**
-   **No query rewrites**: Keep your SQL and start querying both structured and unstructured data with cosine similarity. No learning curve, no vector database alternatives required.

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## **What e6data Helps You Find Today?**

[View the use-case slides](https://docs.google.com/presentation/d/1Zz1U_UGDIyRnJQvWtfp8ChtiluerBWuAhVamupwMEnA/edit?usp=sharing)

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### **SaaS: Churn Signals from Feedback, 10× Faster**

**Before**: Feedback buried in support tickets, surveys, and app reviews. Analysis took weeks. Patterns surfaced too late.**‍**

**After**: e6data semantically searches feedback within OneLake. Support tickets like “UI is confusing” are grouped with “can’t find the button.” Teams act in a day, not weeks. Churn detection moved from reactive to proactive.

### **Finance: Detecting Risk in Chat Logs**

**Before**: Fraud and churn indicators lived in agent notes and chat transcripts. Detection cycles took 30 days.**‍**

**After**: e6data matches new risk signals to semantically similar past chats. Teams query transcripts and account data in one SQL statement. Risk teams act within hours, not weeks.

### **Retail: Spotting Return Reasons in Reviews**

**Before**: Trends like “sizing issues” in reviews took 60 days to detect manually.**‍**

**After**: e6data finds every semantically similar complaint, even when customers phrase it differently. Time-to-insight dropped to under a week. Teams respond faster with better ops and pricing.

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## **How e6data Optimizes Semantic + Structured Queries in Fabric**

-   **Atomic, decentralized architecture**: No driver node. No bottlenecks. Perfect for bursty or high-concurrency environments.  
      
    
-   **Kubernetes-native autoscaling**: Optimize Fabric costs, paying only per actual CPU second, resulting in up to 60% lower total costs.  
      
    
-   **Optimized execution**: Optimized execution leverages vectorized processing, shuffle reduction, and stage fusion for improved semantic search optimization and reduced query latency.  
      
    
-   **Zero governance gap**: Honors Fabric’s security model—row-level filtering, column masking, and IAM integration.

These technical enhancements power data's high-performance vector search even under heavy concurrency and complex queries (e.g. large table scans)

Forget separate stacks. e6data allows **Retrieval-Augmented Generation (RAG)** and **LLM-based analytics** to happen directly on Fabric:

-   SQL to filter deals by ARR + vector search to find meetings hinting at churn  
      
    
-   JOIN structured support ticket metadata with semantically grouped complaints  
      
    

Build LLM apps using live semantic queries over OneLake data

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## **e6data vs Other Alternatives on Fabric**

![e6data vs Other Alternatives on Fabric](/images/blog/Oe6OMpIH3SNZyOjIWlKdUh7W1cE.jpg)

Want to try it? Launch [e6data on Fabric](https://azuremarketplace.microsoft.com/en-us/marketplace/apps/e6data.e6data) (available on Azure Marketplace), run your first SQL + AI query, and experience unified SQL and vector search performance in minutes.

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