In this episode, Justin Parisi is joined by Arindam Banerjee, VP and Technical Fellow at NetApp and Chief Architect of ONTAP, along with Jeff Baxter, VP of Product Marketing, to discuss disaggregated ONTAP, also known as AFX.

The conversation explores how disaggregated ONTAP separates storage compute from storage capacity, allowing customers to scale performance and capacity independently. This architecture is designed for modern workloads such as AI, machine learning, EDA, media and entertainment, and large-scale unstructured data environments.
Key topics include:

• Why AI workloads require asymmetric scaling of compute and capacity
• How disaggregated ONTAP preserves familiar ONTAP data services
• The role of shared capacity pools in reducing storage silos
• How volume moves can become lightweight ownership changes
• The impact of RDMA, nconnect, and pNFS on performance
• Why metadata handling matters for AI, EDA, and M&E workloads
• How file and object duality supports modern AI data pipelines
• How this differs from past ONTAP transitions like 7-Mode to clustered ONTAP
• Why this is still ONTAP, not a separate storage operating system

The episode also discusses how disaggregated ONTAP fits into NetApp’s broader intelligent data infrastructure strategy and why minimizing unnecessary data movement is critical at petabyte and exabyte scale.

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