How NetApp and AWS Are Unlocking ERP Data for Intelligent Insights

One of the biggest challenges for SAP customers is enabling secure, performant access to the vast troves of proprietary ERP data—without replicating it, exporting it, or disrupting mission-critical workloads. NetApp’s latest collaboration with Amazon Web Services (AWS) directly targets that barrier. The newly announced Amazon S3 Access Points for Amazon FSx for NetApp ONTAP allow SAP customers to connect file-resident enterprise data to AWS’s portfolio of AI, ML, and analytics services while keeping that data fully in place on FSx for ONTAP.

This capability is especially significant for SAP environments. SAP HANA, SAP Business Suite, and related workloads running on FSx for ONTAP generate sales, inventory, logistics, HR, financial, and operational data that enterprises increasingly want to feed into generative AI models or Retrieval Augmented Generation (RAG) applications. Historically, operational and regulatory constraints made that difficult. The new S3 Access Points integration aims to remove those constraints entirely.

“By connecting FSx for ONTAP data natively to AWS’s wide range of AI, ML, and analytics services, the new integration unleashes the potential to connect to more than 100 exabytes of enterprise data stored on NetApp systems,” said Pravjit Tiwana, Senior Vice President and General Manager, Cloud Storage and Services at NetApp.

The implications for SAP customers are considerable. They now gain direct, secure pathways from ERP data to AWS GenAI services like Amazon Bedrock and Amazon SageMaker without lifting or copying any data.

A Secure Bridge Between SAP File Data and AWS AI Services

S3 Access Points for FSx for ONTAP function as dedicated, permission-controlled endpoints that expose file data through the Amazon S3 API. That means SAP customers can treat their FSx for ONTAP file systems as if they were S3 buckets, instantly making them compatible with a broad ecosystem of AWS-native AI, ML, analytics, and serverless compute services.

This design effectively turns ONTAP into a unified, hybrid data platform for GenAI workloads. Customers no longer need to move SAP-generated files into separate data lakes before training or querying models. Instead, they can build RAG applications that surface enterprise-specific insights, such as:

• Conversational search across SAP FI/CO financial records
• Automated insight generation from sales or order-to-cash logs
• Predictive recommendations built on procurement, inventory, or supply chain data
• HR and talent insights informed by internal workforce datasets

By eliminating data movement, the approach reduces compliance exposure while preserving ONTAP’s built-in cyber-resilience capabilities such as ransomware detection and immutable snapshots.

Enabling GenAI with Data That Stays in Place

For SAP customers building GenAI or ML applications, the ability to keep ERP data in place is a defining architectural advantage. FSx for ONTAP is already widely deployed for SAP HANA and Business Suite landscapes, making it a familiar platform for Basis, infrastructure, and data management teams. Extending that same platform to GenAI models reduces complexity and supports consistent governance across the SAP stack.

According to IDC’s Jasdeep Singh, “Native integration with cloud-native AI and analytics services and advanced workload management capabilities give enterprises the tools they need to operate efficiently and adapt to evolving requirements.”

What This Means for SAPinsiders

This accelerates AI adoption for SAP teams. Organizations can now build RAG and GenAI applications directly on top of operational SAP data without designing new extraction pipelines. Companies like large retailers and manufacturers already using FSx for ONTAP in hybrid ERP landscapes report faster analytics cycles and reduced data engineering overhead.

This reshapes data governance for enterprise AI. As more SAP customers evaluate cloud-based AI architectures, the ability to leave ERP data in place reduces risk and simplifies compliance. Market trends show rapid growth in AI services consumption across industries, and this integration helps SAP leaders participate without restructuring their storage environments.

This guides how to evaluate future AI infrastructures. SAP professionals should prioritize platforms that minimize data movement, support hybrid-cloud replication, and integrate natively with AI ecosystems like Amazon Bedrock. Early adopters emphasize that success comes from aligning storage, security, and AI engineering teams around shared governance and performance KPIs. This new capability provides a model for how SAP estates can modernize while maintaining operational continuity.