“The long run is already right here,” science fiction author William Gibson as soon as mentioned. “It’s simply not evenly distributed but.” One one who’s seeking to carry knowledge storage into the long run and make it extensively distributed is David Flynn, who’s the CEO and founding father of Hammerspace in addition to a BigDATAwire Particular person to Look ahead to 2025.
Even earlier than founding Hammerspace in 2018, Flynn had an eventful profession in IT, together with creating solid-state knowledge storage platforms at Fusion-iO and dealing with Linux-based HPC techniques. However now as Hammerspace positive aspects traction, Flynn is keen to construct the subsequent era of distributed file techniques and hopefully remedy among the hardest knowledge issues on this planet.
Right here’s our current dialog with Flynn:
BigDATAwire: First, congratulations in your choice as a 2025 BigDATAwire Particular person to Watch! Earlier than Hammerspace, you had been the CEO and founding father of Fusion-io, which SanDisk purchased in 2014. Earlier than that, you had been chief architect at Linux Networx, the place you designed a number of of the world’s largest supercomputers. How did these experiences lead you to discovered Hammerspace in 2018?
David Flynn: It’s a very fascinating trajectory, I feel, that led to the creation of Hammerspace. Early on in my profession, I used to be embedding alternate open-source software program like Linux into tiny techniques like TV set-top bins, company sensible terminals and the like. After which I got here to design lots of the world’s largest supercomputers within the high-performance computing trade that leveraged applied sciences like Linux clustering, InfiniBand, RDMA-based applied sciences.
These two extremes – small embedded techniques versus huge supercomputers – won’t appear to have a ton in frequent, however they share the necessity to extract absolutely the most efficiency from the {hardware}.
This led to the creation of Fusion-io, which pioneered the usage of flash for enterprise software acceleration, which till that time was usually used for embedded techniques in client electronics — for instance, the flash on units like iPods and early cell telephones. I noticed a possibility to take that innovation from the patron electronics world and translate into the info middle, which created a shift away from mechanical arduous drives in the direction of solid-state storage. The difficulty then grew to become that this transition in the direction of solid-state drives wanted extraordinarily quick efficiency; the info wanted to be bodily distributed throughout a set of servers or throughout third celebration storage techniques.
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The introduction of ultra-high-performance flash was instrumental in addressing this problem of decentralized knowledge, and abstracting knowledge from the underlying infrastructure. Most knowledge in enterprises at the moment is unstructured, and it’s arduous for these organizations to search out and extract the worth inside it.
This realization finally led to the creation of Hammerspace, with the imaginative and prescient to make all enterprise knowledge globally accessible, helpful, and indispensable, fully eliminating knowledge entry delays for AI and high-performance computing.
BDW: We’re 20 years into the Large Information increase now, but it surely feels as if we’re at an inflection level with regards to storage. What do you see as the principle drivers this time round, and the way is Hammerspace positioned to capitalize on them?
DF: To essentially thrive on this subsequent knowledge cycle, we’ve acquired to repair the damaged relationship between the info and the info infrastructure the place it’s saved. Enterprises have to assume past storage and moderately how they’ll remodel knowledge entry and administration in trendy AI environments.
Distributors are all competing to supply the efficiency and scale that’s wanted to assist AI workloads. Besides it’s not nearly accelerating knowledge throughput to GPU servers – the core downside is that knowledge pathways between exterior storage and GPU servers get bottlenecked by pointless and inefficient hops within the knowledge path throughout the server node and on the community, whatever the exterior shared storage in use.
The answer right here, which is addressed by Hammerspace’s Tier 0, is using the native NVMe storage which is already included inside GPU servers to speed up AI workloads and enhance GPU utilization. By leveraging the prevailing infrastructure and built-in Linux capabilities, we’re eradicating that bottleneck with out including complexity.
We do that by leveraging the intelligence that’s constructed into the Linux kernel which permits our clients to make the most of the prevailing storage infrastructure they’re already utilizing, with out proprietary shopper software program or different level options. That is along with offering world multi-protocol file/object entry, knowledge orchestration, knowledge safety, and knowledge companies throughout a worldwide namespace.
BDW: You acknowledged on the HPC + AI on Wall Avenue 2023 occasion that we had been all duped by S3 and object storage to surrender the advantages of native entry inherent with NFS. Isn’t the struggle towards S3 and object storage destined to fail, or do you see a resurgence in NFS?
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DF: Let’s be clear—its not about object or file, nor, S3 or NFS. Storage interfaces wanted to evolve to perform scale. S3 happened and have become the default for cloud-scale storage for a superb purpose: older variations of NFS merely couldn’t scale or carry out on the ranges wanted for early HPC and AI workloads.
However that was then. As we speak, NFSv4.2 with pNFS is a unique animal—absolutely matured, built-in into the Linux kernel, and able to delivering huge scale and native efficiency with out proprietary shoppers or advanced overhead. In reality, it’s turn into a normal for organizations that demand excessive efficiency and environment friendly entry throughout massive, distributed environments.
So this isn’t about selecting sides in a file vs. object debate. That framing is outdated. The actual breakthrough is enabling each file and object entry inside a single, standards-based knowledge platform—the place knowledge could be orchestrated, accessed natively, and served by way of whichever interface a given software or AI mannequin requires.
S3 isn’t going away—many apps are written for it. Nevertheless it’s now not the one possibility for scalable knowledge entry. With the rise of clever knowledge orchestration, Tier 0 storage, and trendy file protocols like pNFS, we will now ship efficiency and suppleness with out forcing a selection between paradigms.
The long run isn’t about preventing S3—it’s about transcending the boundaries of each file and object storage with a unified knowledge layer that speaks each languages natively, and places the info the place it must be, when it must be there.
BDW: How do you see the AI revolution of the 2020s impacting the earlier decade’s large advance, which was separating compute and storage? Can we afford to carry large GPU compute to the info, or are we destined to return to shifting knowledge to compute?
DF: The separation of compute and storage made sense when bandwidth was low cost, workloads had been batch-oriented, and efficiency wasn’t tied to GPU utilization. However within the AI period, the place idle GPUs imply wasted {dollars} and misplaced alternatives, that mannequin is beginning to crack.
The problem now isn’t nearly the place the compute or knowledge lives—it’s about how briskly and intelligently you’ll be able to bridge the 2. At Hammerspace, we consider the reply is to not return to outdated habits, however to evolve past inflexible infrastructure with a worldwide, clever knowledge layer.
We make all knowledge seen and accessible in a worldwide file system—regardless of the place it bodily resides. Whether or not your software speaks S3, SMB, or NFS (together with trendy pNFS), the info seems native. And that’s the place the magic occurs: our metadata-driven orchestration engine can transfer knowledge with excessive granularity—file by file—to the place the compute is, with out disrupting entry or requiring rewrites.
So the actual reply isn’t selecting between shifting compute to knowledge or vice versa. The actual reply is dynamic, policy-driven orchestration that locations knowledge precisely the place it must be, simply in time, throughout any storage infrastructure, so AI and HPC workloads keep fed, quick, and environment friendly.
The AI revolution doesn’t undo the separation of compute and storage—it calls for we unify them with orchestration that’s smarter than both alone.
BDW: What are you able to inform us about your self outdoors of the skilled sphere – distinctive hobbies, favourite locations, and so forth.? Is there something about you that your colleagues is perhaps stunned to be taught?
DF: Exterior of labor, I spend as a lot time as I can with my youngsters and household—often on skis or filth bikes. There’s nothing higher than getting out on a mountain or a path and simply having fun with the trip. It’s quick, technical, and slightly chaotic—just about my very best weekend.
That mentioned, I’ve by no means actually separated work from play within the conventional sense. For me, writing software program and inventing new methods to unravel powerful issues is what I’ve all the time beloved to do. I’ve been constructing techniques since I used to be a child, and that curiosity by no means actually went away. Even after I’m off the clock, I’m typically deep in code or sketching out the subsequent thought.
Folks is perhaps stunned to be taught that I genuinely benefit from the artistic course of behind tech—whether or not that’s low-level system design or rethinking how infrastructure ought to work within the AI period. Some of us unwind with hobbies. I unwind by fixing arduous issues.
You’ll be able to learn the remainder of our conversations with BigDATAwire Folks to Watch 2025 honorees right here.