Enterprise Data Strategy

The AI Data Bridge

Unifying legacy file infrastructure with modern S3-native Gen AI and Analytics services.

Protocol Convergence Architecture

Ingest Layer
🏢
On-Prem Data
SnapMirror / NFS
🖥️
User Shares
SMB (Active Directory)
Unified Storage Hub
📦

FSx for NetApp ONTAP

Dedupe Tiering Snapshots
Native S3 API Enabled
S3 Protocol Consumers
✨
Amazon Bedrock

Retrieval-Augmented Gen (RAG)

🔍
AWS Athena

Serverless SQL on Files

🧪
SageMaker

ML Model Training

"The complexity of managing separate data silos is eliminated. S3 endpoints allow Gen AI to read live enterprise data without ETL."

Efficiency of Unified Protocol

By using S3 endpoints, you eliminate the "Data Translation Tax". Organizations save an average of 40% on operational costs by removing data-syncing scripts.

Consumer Adoption by Protocol

While NFS/SMB still dominate ingest, S3 is the mandatory standard for over 90% of modern AWS Analytics and AI/ML services.

⚡

Zero-ETL AI

Feed Bedrock agents directly from production shares. No data movement, no delay.

💎

Serverless BI

Run Athena queries on parquet files stored on FSx for ONTAP via the S3 endpoint.

🔒

Unified Security

Apply S3 bucket policies alongside NetApp export policies for dual-layer protection.

📈

Storage Reuse

Stop paying for two copies of data. Store it once, access via any protocol.