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. It brings structured and unstructured data under one roof. But some teams still struggle to query across formats without jumping through hoops:

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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

e6data is a lakehouse compute engine which is now integrated with Fabric. It brings fast, unified querying—structured and unstructured, in the same SQL statement.

• 10x performance: Atomic scaling, coordinator-free architecture, 60% lower cost. 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?

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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

Want to try it? Launch e6data on Fabric (available on Azure Marketplace), run your first SQL + AI query, and experience unified SQL and vector search performance in minutes.

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