“The longer term is already right here,” science fiction author William Gibson as soon as stated. “It’s simply not evenly distributed but.” One one that’s seeking to deliver information storage into the long run and make it broadly distributed is David Flynn, who’s the CEO and founding father of Hammerspace in addition to a BigDATAwire Individual to Look ahead to 2025.
Even earlier than founding Hammerspace in 2018, Flynn had an eventful profession in IT, together with growing solid-state information storage platforms at Fusion-iO and dealing with Linux-based HPC methods. However now as Hammerspace beneficial properties traction, Flynn is keen to construct the following technology of distributed file methods and hopefully clear up a few of the hardest information issues on the earth.
Right here’s our latest dialog with Flynn:
BigDATAwire: First, congratulations in your choice as a 2025 BigDATAwire Individual 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 extremely fascinating trajectory, I believe, 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 methods like TV set-top bins, company good terminals and the like. After which I got here to design lots of the world’s largest supercomputers within the high-performance computing business that leveraged applied sciences like Linux clustering, InfiniBand, RDMA-based applied sciences.
These two extremes – small embedded methods versus large supercomputers – won’t appear to have a ton in widespread, 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 utility acceleration, which till that time was usually used for embedded methods in shopper electronics — for instance, the flash on units like iPods and early cell telephones. I noticed a possibility to take that innovation from the buyer electronics world and translate into the information heart, which created a shift away from mechanical arduous drives in direction of solid-state storage. The problem then grew to become that this transition in direction of solid-state drives wanted extraordinarily quick efficiency; the information wanted to be bodily distributed throughout a set of servers or throughout third celebration storage methods.
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The introduction of ultra-high-performance flash was instrumental in addressing this problem of decentralized information, and abstracting information from the underlying infrastructure. Most information in enterprises at this time is unstructured, and it’s arduous for these organizations to seek 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 information globally accessible, helpful, and indispensable, fully eliminating information entry delays for AI and high-performance computing.
BDW: We’re 20 years into the Huge Information increase now, nevertheless it feels as if we’re at an inflection level with regards to storage. What do you see as the primary drivers this time round, and the way is Hammerspace positioned to capitalize on them?
DF: To essentially thrive on this subsequent information cycle, we’ve acquired to repair the damaged relationship between the information and the information infrastructure the place it’s saved. Enterprises must assume past storage and relatively how they will remodel information entry and administration in fashionable 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 information throughput to GPU servers – the core drawback is that information pathways between exterior storage and GPU servers get bottlenecked by pointless and inefficient hops within the information path inside 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 present 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 prospects to make the most of the present storage infrastructure they’re already utilizing, with out proprietary consumer software program or different level options. That is along with offering international multi-protocol file/object entry, information orchestration, information safety, and information 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 combat in opposition to 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 an excellent cause: 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. At the moment, NFSv4.2 with pNFS is a special animal—absolutely matured, built-in into the Linux kernel, and able to delivering large scale and native efficiency with out proprietary shoppers or complicated overhead. In truth, it’s develop into a regular for organizations that demand excessive efficiency and environment friendly entry throughout giant, distributed environments.
So this isn’t about selecting sides in a file vs. object debate. That framing is outdated. The true breakthrough is enabling each file and object entry inside a single, standards-based information platform—the place information might be orchestrated, accessed natively, and served by means of whichever interface a given utility or AI mannequin requires.
S3 isn’t going away—many apps are written for it. However it’s now not the one choice for scalable information entry. With the rise of clever information orchestration, Tier 0 storage, and fashionable file protocols like pNFS, we will now ship efficiency and suppleness with out forcing a alternative between paradigms.
The longer term isn’t about combating S3—it’s about transcending the bounds of each file and object storage with a unified information layer that speaks each languages natively, and places the information 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 deliver large GPU compute to the information, or are we destined to return to transferring information 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 information lives—it’s about how briskly and intelligently you may bridge the 2. At Hammerspace, we imagine the reply is to not return to outdated habits, however to evolve past inflexible infrastructure with a worldwide, clever information layer.
We make all information seen and accessible in a worldwide file system—regardless of the place it bodily resides. Whether or not your utility speaks S3, SMB, or NFS (together with fashionable pNFS), the information seems native. And that’s the place the magic occurs: our metadata-driven orchestration engine can transfer information 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 transferring compute to information or vice versa. The true reply is dynamic, policy-driven orchestration that locations information 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 may be shocked to study?
DF: Exterior of labor, I spend as a lot time as I can with my children and household—normally on skis or dust bikes. There’s nothing higher than getting out on a mountain or a path and simply having fun with the experience. It’s quick, technical, and slightly chaotic—just about my superb weekend.
That stated, 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 robust issues is what I’ve at all times liked to do. I’ve been constructing methods 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 usually deep in code or sketching out the following thought.
Folks may be shocked to study that I genuinely benefit from the inventive 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 may learn the remainder of our conversations with BigDATAwire Folks to Watch 2025 honorees right here.