NetApp Powers the Future of AI with Intelligent Data Infrastructure

⇨ NetApp is enhancing its intelligent data infrastructure to support AI innovation, emphasizing the importance of governable, trusted, and traceable data for successful AI implementation.

⇨ Recent research indicates that 75% of companies prioritize robust data infrastructure for effective AI, highlighting the need for organizations to manage data efficiently across hybrid multi-cloud environments.

⇨ NetApp's new capabilities, including NVIDIA DGX SuperPOD Storage Certification and a disaggregated storage architecture, aim to optimize data management for large AI projects and improve performance while reducing costs.

NetApp® (NASDAQ: NTAP), the intelligent data infrastructure company and SAP solution build and global technology partner, recently announced new developments in its collaboration with industry leaders to accelerate AI innovation. By providing the intelligent data infrastructure required to make GenAI work, NetApp is helping organizations tap into one of the most important developments for business and IT in the last decade.

GenAI powers practical and highly visible use cases for business innovation such as generating content, summarizing large amounts of information, and responding to questions. The key to success in the AI era is mastery over governable, trusted, and traceable data.

Recent SAPinsider research shows that companies are recognizing the importance of data management in operationalizing AI. The research report AI: State of Adoption 2024 uncovered that 75% of companies deem robust data infrastructure to collect, store and manage data efficiently as important or very important for AI implementation.

In September 2024, NetApp CEO George Kurian kicked off NetApp INSIGHT 2024 with an expansive vision of this era of data intelligence. A large part of the AI challenge is a data challenge, and Kurian laid out a vision for how intelligent data infrastructure can ensure the relevant data is secure, governed, and always updated to feed a unified, integrated GenAI stack.

At NetApp INSIGHT, NetApp unveiled further innovations in intelligent data infrastructure, including a transformative vision for AI running on NetApp ONTAP®, the leading operating system for unified storage. Specifically, NetApp’s vision includes:

• NVIDIA DGX SuperPOD Storage Certification for NetApp ONTAP: NetApp has begun the NVIDIA certification process of NetApp ONTAP storage on the AFF A90 platform with NVIDIA DGX SuperPOD AI infrastructure, which will enable organizations to leverage industry-leading data management capabilities for their largest AI projects. This certification will complement and build upon NetApp ONTAP’s existing certification with NVIDIA DGX BasePOD. NetApp ONTAP addresses data management challenges for large language models (LLMs), eliminating the need to compromise data management for AI training workloads.

• Creation of a global metadata namespace to explore and manage data in a secure and compliant fashion across a customers’ hybrid multi-cloud estate to enable feature extraction and data classification for AI. NetApp separately announced a new integration with NVIDIA AI software that can leverage the global metadata namespace with ONTAP to power enterprise retrieval augmented generation (RAG) for agentic AI.

• Directly integrated AI data pipeline, allowing ONTAP to make unstructured data ready for AI automatically and iteratively by capturing incremental changes to the customer data set, performing policy driven data classification and anonymization, generating highly compressible vector embeddings and storing them in a vector DB integrated with the ONTAP data model, ready for high scale, low latency semantic searches and retrieval augmented generation (RAG) inferencing.

• A disaggregated storage architecture that enables full sharing of the storage backend, which maximizes utilization of network and flash speeds and lowers infrastructure cost, significantly improving performance while economizing rack space and power for very high-scale, compute-intensive AI workloads like LLM training. This architecture will be an integral part of NetApp ONTAP, so it will get the benefit of a disaggregated storage architecture but still maintain ONTAP’s proven resiliency, data management, security and governance features.

• New capabilities for native cloud services to drive AI innovation in the cloud. Across all its native cloud services, NetApp is working to provide an integrated and centralized data platform to ingest, discover and catalog data. NetApp is also integrating its cloud services with data warehouses and developing data processing services to visualize, prepare and transform data. The prepared datasets can then be securely shared and used with the cloud providers’ AI and machine learning services, including third party solutions.

“Organizations of all sizes are experimenting with GenAI to increase efficiency and accelerate innovation,” said Krish Vitaldevara, Senior Vice President, Platform at NetApp. “NetApp empowers organizations to harness the full potential of GenAI to drive innovation and create value across diverse industry applications. By providing secure, scalable, and high-performance intelligent data infrastructure that integrates with other industry-leading platforms, NetApp helps customers overcome barriers to implementing GenAI. Using these solutions, businesses will be able to more quickly and efficiently apply their data to GenAI applications and outmaneuver competitors.”

NetApp continues to innovate with the AI ecosystem:

• Domino Data Labs chooses Amazon FSx for NetApp ONTAP: To advance the state of machine learning operations (MLOps), NetApp has partnered with Domino Data Labs, underscoring the importance of seamless integration in AI workflows. Domino now uses Amazon FSx for NetApp ONTAP as the underlying storage for Domino Datasets running in Domino Cloud platform to provide cost-effective performance, scalability, and the ability to accelerate model development. In addition to Domino using FSx for NetApp ONTAP, Domino and NetApp have also begun joint development to integrate Domino’s MLOps platform directly into NetApp ONTAP to make it easier to manage the data for AI workloads.

• General Availability of AIPod with Lenovo for NVIDIA OVX: Announced in May 2024, the NetApp AIPod with Lenovo ThinkSystem servers for NVIDIA OVX converged infrastructure solution is now generally available. This infrastructure solution is designed for enterprises aiming to harness generative AI and RAG capabilities to boost productivity, streamline operations, and unlock new revenue opportunities.

• New capabilities for FlexPod AI: NetApp is releasing new features for its FlexPod AI solution, the hybrid infrastructure and operation platform that accelerate the delivery of modern workloads. FlexPod AI running RAG simplifies, automates, and secures AI applications, enabling organizations to leverage the full potential of their data. With Cisco compute, Cisco network, and NetApp storage, customers experience lower costs, efficient scaling, faster time to value, and reduced risks.

What this means for SAPinsiders

Share your data management strategies. The focus on enterprise data management is intensifying with the proliferation of 5G, IoT, AI/ML and other transformative technologies. SAP customers are increasingly looking for new data management models for the storage, migration, integration, governance, protection, transformation, and processing of all kinds of data ranging from transactional to analytical. Balancing the risks, compliance needs, and costs of data management in SAP HANA on-premise and on the cloud while also providing reliable, secure data to the organization is increasingly important to the business We will be releasing the 2025 Data Management Strategies research report in February 2025. Contribute to the research by completing this survey: https://www.research.net/r/DataMgt25.

Think through data storage strategy to support AI. AI models can leverage all types of data (e.g., text, images, audio, transactional data). Ensure that your data infrastructure can store and manage a mix of structured, semi-structured, and unstructured data. Design a scalable data lake (for unstructured and raw data) or data warehouse (for structured and cleaned data) to store large volumes of data efficiently. Data lakes or cloud-based storage solutions are useful for AI workloads. Use partitioning, indexing, and caching techniques to optimize data retrieval speeds for AI processes, especially when dealing with large datasets.

Don’t overlook data governance and compliance. Establish robust data governance policies to ensure proper management, ownership, and usage of data. Define roles such as data stewards and data owners to maintain data quality and governance across the organization. Ensure that data infrastructure complies with industry-specific regulations (e.g., GDPR, CCPA, HIPAA). This is especially important for sensitive data used in AI models, such as personal information or health data. Implement robust security measures such as encryption, access controls, and data anonymization to protect sensitive data. Use privacy-preserving techniques like differential privacy for AI use cases involving sensitive data.