Storage Blending: The Evolving Role of HDD and SSD in Data Systems for an AI and Analytics Era
Santa Clara Ballroom
Tue Sep 16 | 9:25am
Abstract
As the rapid expansion of AI and analytics continues, storage system architecture and total cost of ownership (TCO) are undergoing significant transformation. Emerging technologies such as HAMR in rotating storage and high-capacity, data center-grade QLC in flash promise to redefine the landscape for both hyperscale and OEM data storage solutions. But what will that evolution look like?
This panel brings together a distinguished group of industry experts to explore a wide range of perspectives—from hyperscaler and OEM viewpoints to strategic insights from both solid-state and rotating media technologists. Together, they will examine how shifting performance, density, and cost dynamics are reshaping storage architecture in an AI-driven future.
Panel Lineup:
* Madhavan Ravi – Hardware Systems Engineer, Meta. Designs and deploys storage hardware for exabyte-scale distributed systems.
* Curtis Ballard – Distinguished Technologist, HPE. Focuses on storage architecture and next-generation technology strategy.
* Currie Munce – Senior strategist for capacity storage architecture, Micron Technology; former Vice President.
* Mohamad El-Batal – Chief Technologist, Cloud Systems Group, Seagate Technology.
* Moderator: Steven Wells – Retired Fellow of SSD storage architecture (Intel, KIOXIA, and Micron).
Related Sessions
Keynote
StorageAI - 1 Year of Progress
After only 1 year, SNIA's StorageAI has made incredible progress.
From workload performance to accelerator I/O to efficiency and sustainability to data movement to much more, the rapid pace of development has driven the industry towards greater open standardization. When the industry collaborate to solve foundational infrastructure problems, everyone scales faster - vendors, customers, end users, and entirely adjacent industries.
Join the Chair as we discuss the incredible work that's been done in a very short period of time and project the need for collaborative work as we move into the future.
Keynote
Not All QLC Is Created Equal: Designing Flexible Architectures for NAND Diversity
Hyperscalers face increasingly complex challenges as they integrate diverse NAND media with modern filesystems across many demanding workloads. Meeting these challenges requires flexible, software-defined architectures that support mixed-mode operation, intelligent data placement, and workload-aware optimization. It also requires advanced performance metrics and analysis tools to enable rigorous evaluation, expose device-level tradeoffs, and predict failures earlier.
Drawing on engineering experience with hyperscalers, this keynote explores why not all QLC is created equal—and why effective deployment demands tailored integration strategies. The talk will argue for deeper hardware-software co-design, from power and cooling to filesystem structure, as the foundation for the next generation of resilient, high-performance storage systems.
Keynote
Lessons From the Front Lines of Enterprise AI: Real World Impact, Infrastructure Realities, and What Engineers Must Understand
Artificial Intelligence is often positioned as the next phase of hyperscale computing—but most Enterprises operate under a very different set of constraints. Unlike cloud native companies, Enterprises face a complex intersection of legacy systems, economic pressures, and real-world operational constraints—factors often invisible to engineers building the underlying technologies. This session focuses on the realities of deploying AI at scale across the hybrid cloud with environments that do not resemble hyperscale architecture.
In this session we will examine how three forces are reshaping Enterprise architecture. First, the economic model: inference, retrieval, and orchestration create ongoing non-linear costs making efficiency—not scale—the primary design principle. Second, the technical reality: retrieval-augmented generation (RAG) requires new compute with latency and security constraints, making hybrid cloud more essential to balance the tradeoffs of performance, cost, and control. Third, the operational considerations: legacy systems, regulatory requirements, and the inability to trust AI with sensitive data fundamentally shape what can be deployed into production.
This session will focus on lessons learned from managing AI workloads constrained by database bottlenecks, cloud capacity shortages, inconsistent supply of hardware components, and the operational challenges of model drift, quantization choices, and cloud-specific performance variability. It reveals why Enterprise buyers prioritize reliability, predictable supply, and cost efficiency over cutting edge performance—and how misaligned industry innovation can unintentionally disadvantage non-hyperscale customers.
Ultimately, this keynote aims to bridge the gap between AI system and component builders and AI operators—illustrating the practical consequences of design decisions, the constraints faced by real customers, and the opportunities for the industry to build more durable, accessible, and economically sustainable AI infrastructure.
