A Unified Data Fabric is an architectural approach that integrates data discovery, access, governance, and movement across distributed environments into a single intelligent layer. For AI workloads, it solves the fundamental challenge that most enterprises face: data is scattered, ungoverned, and difficult to access quickly enough to fuel production AI applications like RAG, agentic AI, and AI factories. ¹⁾

An enterprise AI fabric unifies multiple capabilities into a coherent operating layer:

• Data pipelines — Batch and streaming ingestion, transformation, testing, and lineage tracking extended to features and embeddings
• Feature stores — Reusable, governed ML features shared across teams and models
• Vector stores — Embedding storage and retrieval for RAG and semantic search
• Metadata catalog — Global, automatically maintained catalog with semantic enrichment of file content
• Governance layer — Policy-driven access controls, lineage tracking, and compliance enforcement
• Orchestration — Unified management of the full AI asset lifecycle from data preparation to model deployment ²⁾

NetApp's AI Data Engine (AIDE), co-engineered with NVIDIA and integrated with the NVIDIA AI Data Platform reference design, is a storage-integrated AI data service:

• Global metadata catalog — Automatically created and continuously updated; analyzes file content for semantic enrichment in place (without moving data)
• Data discovery and curation — Semantic search across on-premises and cloud environments
• Policy-driven guardrails — Automatic protection of sensitive data with access controls
• Real-time vectorization — Integrated vector generation for GenAI and RAG workloads
• ONTAP integration — Built on NetApp's enterprise storage platform for unified management

Launched to lighthouse customers in March 2026 with broad availability in early summer. ³⁾

• IBM — Data Fabric solutions integrating Watson and Cloud Pak for Data across hybrid cloud
• Informatica — CLAIRE AI-powered data management with intelligent data fabric capabilities
• Snowflake — Evolving toward unified data and AI platform with feature store and model registry
• Databricks — Lakehouse architecture combining data lake and warehouse with MLflow integration
• Azure Synapse — Microsoft's analytics service integrating data integration, warehousing, and big data analytics
• Salesforce — Data Cloud providing unified customer data for AI-powered CRM ⁴⁾

• Eliminates data silos — Single access layer across cloud, on-premises, and edge storage
• Accelerates AI projects — Instant discovery of relevant training data with semantic search
• Ensures governance — Traceable, explainable models with robust versioning for compliance
• Reduces complexity — Replaces disconnected point tools with integrated pipelines
• Supports production AI — Handles the data infrastructure requirements of RAG, agentic AI, and continuous model retraining ⁵⁾

Organizations typically struggle not with models or compute, but with data. Unstructured, ungoverned data scattered across global estates is impossible to access quickly and safely enough to fuel production GenAI workloads. A unified data fabric addresses this by providing:

• Clarity on what data exists and where it lives
• Efficient data transformation and preparation
• Secure, governed access for AI consumption
• Continuous synchronization as data changes ⁶⁾

• AWS SageMaker
• AI Superfactory