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09032026 - TechTalks Recap: Know Your Data and Trust Your AI 🤖
If you missed last week’s TechTalks, Ashish Gupta and I dug into a practical question many teams are working through right now: before you ask AI to make decisions with your data, do you really know what data you have, where it lives, and whether it can be trusted? The short answer is that AI-ready data needs more than a model and a prompt. It needs context. What you missed 👇 🔍 Start with visibility. Data is everywhere: business applications, file shares, email, SaaS platforms, cloud, and legacy systems. You cannot govern—or use—what you cannot see. 🏷️ Classify before you accelerate. Knowing which data is sensitive, stale, duplicated, or governed by a policy is foundational to making it safely available for AI use cases. 🧠Context makes AI more useful. A customer record, an invoice, an email, and a policy document can be related—but only when the data has the right semantic context. That is what helps AI produce relevant, explainable results. ✅ Turn insight into action. The goal is not to lock down every dataset. It is to prioritize the right actions: reduce risk, meet governance requirements, and make the right data available for the right AI initiative. One point that stood out for me: data intelligence is not just an AI conversation. It is a practical way to improve visibility, reduce exposure, and make better decisions across security, governance, and infrastructure teams. Watch on demand 🎥 Want to see the examples and hear the full conversation? Watch “Data Intelligence Impact: Know Your Data and Trust Your AI” on demand. The on-demand session is 58 minutes and covers how discovery, classification, context, and governance can help establish a trustworthy foundation for AI. If you are starting an AI initiative, a good first step is simple: get a clearer picture of your data landscape. From there, you can decide what to protect, what to govern, and what is ready to put to work. 💬 💬 What is the first data challenge your team needs to solve before moving an AI use case forward? Let us know!Ask Us Everything Recap: Bringing Enterprise Storage Simplicity to Azure
What happens when you take the enterprise storage capabilities IT teams rely on and deliver them as a fully managed, Azure-native experience? That question was at the heart of our latest Ask Us Everything, focused on Everpure Cloud Azure Native for Azure VMs. And judging by the questions from attendees, storage admins and cloud practitioners are thinking beyond simply moving workloads to Azure. They’re asking how to get there without trading familiar enterprise capabilities for higher costs, more operational overhead, or another collection of storage tiers to manage. Everpure experts David Stamen, Nick Scuola, and Joe Houghes dug into those questions—and the conversation surfaced several useful takeaways. “Azure already has storage. What makes Everpure different?” Cloud storage can mean navigating different offerings, tiers, feature sets, and regional availability. Joe contrasted that with the enterprise storage model Everpure customers know: capabilities delivered consistently through the Purity OS, rather than requiring administrators to continually determine which storage option provides which feature. Everpure Cloud for Azure Native brings capabilities including thin provisioning, deduplication, compression, performance, and availability into an experience designed to operate natively as an Azure service. Customers can deploy and consume it through Azure Marketplace, and get benefits they’re used to with their on-prem Everpure, all without managing the underlying storage infrastructure themselves. That distinction matters: IT gets enterprise-grade block storage, without turning cloud storage into another infrastructure management project. “How do I balance performance with cost?” Attendees naturally wanted to know about performance tiers—and the answer illustrated the service’s approach to simplicity. Instead of selecting among traditional on-prem array models or architecting around underlying infrastructure, customers simply choose the performance they need. The experts described a slider-based model for adjusting storage-pool performance, with Everpure handling everything behind the service. That abstraction becomes particularly valuable as Azure evolves. The panel discussed how underlying Azure storage improvements have previously delivered both performance gains and lower infrastructure costs. With a managed service, Everpure can incorporate infrastructure changes behind the scenes, rather than requiring customers to redesign their environments every time the cloud platform changes. As Nick summarized, the objective is the outcome: deliver the service level that customers need, while Everpure handles how it gets delivered. “Can cloud storage actually help reduce our Azure spend?” Cost was a recurring theme. We polled attendees, and half responded that they were “very concerned” about their Azure storage spend. One important lever that can help lower costs is data reduction. Thin provisioning, deduplication, and compression can reduce the physical capacity required, although results depend heavily on workload and data type. The experts noted that the majority of traditional application data benefit greatly from data reduction capabilities built into Everpure Cloud, while already-compressed or encrypted data naturally offer less reduction. The consumption model is simplified, too. Everpure Cloud for Azure Native combines the service and underlying infrastructure into a Marketplace purchase rather than separating software licensing from Azure infrastructure costs. Customers simply choose how much capacity and performance they need, and then receive it as one service and one bill. “Does this simplify my storage management job?” Perhaps the most important answer of the session: Yes! The experts described storage deployments that can be stood up in hours rather than turning implementation into a weeks- or months-long project. For example: once a storage pool is created, volumes can be connected to workloads using Azure VM extensions, simplifying the process of getting storage in front of an application. For experienced storage practitioners, that may be the biggest shift. Everpure Cloud for Azure Native isn’t about recreating every storage-management task in Azure. It’s about abstracting more of those tasks away so teams can concentrate on all their other work: applications, data, migrations, automation, and new initiatives. Or, as Nick put it during the session: the goal is to help free up customers to manage their applications and data—not their storage. That’s a cloud operating model storage admins can get behind. Check out this and all our other Ask Us Everything sessions. And, keep the conversation going by jumping into the Everpure Community.The Idea That Was Supposed to Fail
Why DirectFlash and Evergreen//One suddenly look a lot smarter in a world of NAND and DRAM price shocks Dmitry Gorbatov Mar 20, 2026 Important Note for my readers: Writing this piece took me a lot longer than I normally spend on a post. It took a lot of reading and research. Many articles and blogs were written on the subject before NAND and DRAM costs went crazy. The dry-humor version is that the storage industry spent years insisting flash was just disk with better manners, and then acted surprised when the underlying physics eventually asked to speak with management. Now, let’s get to it. I can still picture the room. It wasn’t anything special — just another corporate competitive training session, the kind you’ve sat through many times if you’ve spent enough years in enterprise tech. This was at NetApp, in 2015 or 2016, back when flash was still a question mark. Not if, but how. The industry had not fully committed yet, and everyone was trying to figure out what role it would play. The presenter clicked to the next slide, paused for a second, and said something that stuck with me in a way most of those sessions never do: “Pure Storage is crazy! They’re building their own flash modules. That’s stupid. It’s not sustainable. They won’t survive.” It wasn’t said for effect. There was no dramatic pause afterward, no attempt to persuade. It was delivered as a simple, almost obvious conclusion. And to be fair, it felt obvious. Because the entire storage industry operated on a shared assumption: you didn’t build components, you assembled them. You relied on a mature ecosystem of suppliers who specialized in drives, storage controllers, and memory, and you focused your differentiation on software features and integration. That was the efficient path. That was the scalable path. That was how serious companies behaved. What Pure was proposing at the time — what would later become Everpure — felt like a deviation from that logic. Building your own flash modules didn’t just introduce complexity; it seemed to reject the economic advantages of the broader supply chain. It looked like a risk without a clear payoff. So the conclusion made sense. Until it didn’t. Looking Back, Differently If I think back to that training session now, I do not really see it as a moment where someone was foolish. I see it as a moment where the industry was trapped inside the logic of its own assumptions. If you believe flash should look like disk, then building your own flash modules sounds silly. If you believe storage is just a sequence of refresh cycles, then a model built around non-disruptive evolution sounds unnecessary. If you believe component pricing will keep trending in the right direction forever, then architectural efficiency feels like an academic luxury. But once those assumptions start to crack, the logic changes. And when it changes, the things that once looked eccentric start to look oddly prescient. A Change You Don’t Notice Right Away For years, nothing about that statement felt particularly worth revisiting. The industry moved forward in predictable ways. Flash became mainstream. Performance improved. Density increased. Vendors competed on features, benchmarks, and price points. The conversations most of us had with customers followed familiar patterns. If anything, the abstraction layers built around flash made things easier to consume. SSDs behaved like faster disks — and that was good enough. There is a reason they showed up in familiar HDD form factors. The industry was trying to preserve the old world while sneaking in a new medium. Keep the slots. Keep the enclosures. Keep the assumptions. Change as little as possible. That made adoption easier, but it also buried the problem. Because flash is not a disk. It never was. It does not behave like one, and it does not particularly enjoy being treated like one. The only reason the illusion worked is because the industry built a fairly elaborate translation layer to maintain it. That translation layer is where the story really starts. The Trick That Made Flash Look Simple When commodity SSDs became the standard way to bring flash into enterprise storage, they depended on a piece of internal firmware called the Flash Translation Layer, or FTL. Its job was deceptively simple: make raw NAND look like a disk. That sounds harmless enough until you think about what that actually requires. NAND cannot just overwrite data in place the way the rest of the stack would like it to. It has to handle erase cycles, wear leveling, garbage collection, bad block management, and the constant translation between logical addresses and physical locations on the media. So every SSD became its own little self-contained world, complete with its own controller, its own metadata tables, and its own DRAM to keep track of everything. In other words, every drive became a tiny independent computer, making local decisions in isolation. That design solved the adoption problem. It did not solve the architecture problem. For a while, the tradeoff seemed worth it. The drives were fast enough, the packaging was familiar, and the whole system kept pretending that flash was just a much nicer version of disk. But what looked neat and modular at small scale turned out to be awkward and expensive at enterprise scale. And that is where the “stupid” decision begins to look a lot smarter. What Commodity SSDs Actually Drag Along With Them The more I researched this topic (and believe me I did), the more I realized how much of the industry got comfortable with an abstraction that was doing a lot of quiet damage. Commodity SSDs carry four structural inefficiencies that matter much more today than they did when pricing was stable. Trapped DRAM. Every SSD maintains its own mapping tables, so large-scale systems end up carrying a remarkable amount of DRAM inside the drives themselves. That memory is necessary for the SSD to function, but it does not really help the array think globally. It is duplicated overhead, repeated again and again, drive by drive. In a petabyte-scale system, that is not a rounding error. It is cost, power, and complexity hiding in plain sight. Unpredictable Latency. Garbage collection inside a traditional SSD happens when the drive decides it needs to happen. When that occurs, the drive may become temporarily less responsive, and in an array full of independent drives, those little stalls start to show up as tail-latency spikes. The system is always vulnerable to one drive having a private crisis at exactly the wrong time. Write Amplification. Because the SSD does not really understand the workload or the data structures above it, it moves data more often than necessary. More movement means more writes. More writes mean more wear. More wear means the media gets consumed faster than it should. Over-provisioning. Every SSD holds back some raw capacity for its own housekeeping and spare-cell management, but that reserved space is siloed. The array cannot use it intelligently across the system because each drive is managing its own private affairs. None of this sounded especially dramatic when NAND kept getting cheaper and the economics of flash kept improving. It sounded like engineering trivia. The sort of thing infrastructure people argue about while everyone else waits for the quote. Today it is not trivia. Today it is exposure. Why AI Made This Suddenly Everyone’s Problem For years, one of the quiet assumptions in enterprise IT was that storage capacity would continue to become cheaper and more abundant over time. Not perfectly, not smoothly, but predictably enough that the inefficiencies of the underlying architecture could be tolerated. That assumption is now not only under pressure, it is getting decimated. AI did not just create a new category of interesting workloads. It created a global appetite for silicon that is large enough to bend supply curves. The cute part of AI is easy to mock. The cat kicking the T-Rex. The surreal generated videos. The deepfakes that make you look twice and then sigh a little for civilization. But behind every one of those outputs is a less funny reality: extraordinary consumption of DRAM, NAND, GPUs, and supporting infrastructure. The novelty at the edge is powered by very serious resource demand at the core. And that demand is landing directly on the components enterprise storage depends on. This is the part customers are beginning to feel in ways that are no longer abstract. Expansion quotes do not look as comfortable as they once did. Refresh cycles feel more expensive. Delivery windows stretch. Budgets built on assumptions from even two years ago suddenly need more explaining than anyone wanted. There is a tendency to call this inflation because that is the easiest word available. It is not really inflation. It is supply and demand, with a side of semiconductor reality. And that matters, because a traditional SSD array is exposed to both sides of the problem at once. It is exposed to NAND because that is the medium you are buying, and it is exposed to DRAM because every SSD drags its own DRAM overhead along for the ride. When those two markets tighten at the same time, the cost of the architecture gets hit twice. That is not just a technical nuance. That is economics. Revisiting the “Stupid” Decision This is where the old training-room comment starts to age badly. Because what looked like unnecessary vertical integration was really a decision to stop pretending flash was a disk and start treating it like what it actually is: semiconductor media with very specific physical behaviors that should be managed at the system level, not hidden inside dozens of drives. That is the DirectFlash idea in plain English. Take the Flash Translation Layer out of the individual drive. Pull media management into the operating environment. Let Purity manage flash globally instead of leaving each device to improvise its own local strategy. That changes more than performance charts. It means metadata no longer has to be duplicated and trapped inside every SSD. It means wear leveling can happen across the full system instead of inside the borders of a single device. It means bad block handling, garbage collection, and data placement can be coordinated with global context. It means the platform can see the difference between data that should live together and data that should not, which dramatically reduces unnecessary movement and lowers write amplification. And when write amplification drops, the economics change. The NAND lasts longer. The useful life of the media extends. Lower-endurance flash, like QLC, becomes viable for serious enterprise use because the software is smart enough not to abuse it. The system extracts more useful work from the same raw silicon. That is not just clever engineering. That is insulation from volatility. The reason this matters now is that DirectFlash changes the ratio between the silicon you buy and the value you get from it. If the rest of the market is paying more for NAND and more for DRAM, an architecture that reduces trapped DRAM, minimizes wasted writes, extends media life, and packs far more capacity into far denser modules is not just elegant. It is economically defensive. This is where the old “they build their own flash” criticism misses the point. Building your own flash modules was never the point by itself. The point was controlling the relationship between software and media well enough to eliminate the inefficiencies the commodity model had normalized. Why Purity Is the Real Story DirectFlash makes for a good visual. It is a module. You can point to it. You can talk about density and reliability and the fact that a 150TB module can do work that would have required a small army of traditional devices not all that long ago. But the real story is Purity Operating Environment, i.e. software. Purity is where the architectural bet pays off. It is what turns raw NAND into a coordinated system instead of a pile of politely disagreeing SSDs. Because Purity sees the entire media pool, it can write more intelligently. It can group data with similar expected lifespans together, so that when a snapshot or a temporary workload disappears, whole regions of storage can be retired cleanly instead of forcing background reshuffling of still-live data. That reduces unnecessary churn. Less churn means fewer writes. Fewer writes mean longer media life. Because Purity sees when a NAND die is busy with an erase or program cycle, it can avoid letting that become a host-visible performance problem. RAID-3D and system-level awareness allow the platform to reconstruct data from parity rather than simply waiting for a busy drive to get its act together. The end result is deterministic performance rather than a roulette wheel of occasional latency spikes. Because Purity owns media management globally, the over-provisioning and spare resources are no longer trapped in per-drive silos. The system can use them strategically. I know that all of this sounds a bit scientific, and to be fair, it is. I did spend over 7 years working for Everpure and a few weeks researching for this post. I wanted to sit with that science for a bit. Where the Economics Start to Matter The moment component pricing becomes unpredictable, architecture stops being an engineering preference and starts becoming a financial strategy. That is the part that matters most to customers right now. A traditional buying model assumes that at some point you will hit a refresh cycle, a capacity wall, or a migration event that forces a purchase whether the market timing is good or terrible. You buy when you have to buy. If NAND is expensive, that is unfortunate. If DRAM is expensive too, even better, because apparently the universe enjoys symmetry. That is what makes the combination of DirectFlash and Evergreen so important. DirectFlash reduces the amount of waste, duplication, and premature wear in the system. Evergreen removes the old habit of tying innovation to forklift replacement. Controllers evolve. Capacity can be consolidated into denser modules over time. Data stays in place. The customer is not forced into rebuying the whole environment every few years just to remain current. That already changes the economics. But it still leaves one more question: who is carrying the price risk? And this is where Evergreen//One matters more than ever. The Part I Actually Wanted to Get To Evergreen//One is not just a consumption model. It is not just a nicer way to finance storage. It is a mechanism for moving volatility away from the customer. That is the conclusion I wanted to earn, not just declare. When NAND and DRAM prices start climbing, most traditional models push that turbulence straight into the customer’s planning cycle. The customer eats the increase, absorbs the uncertainty, and tries to explain to the business why the infrastructure line now behaves like it has a gambling problem. Evergreen//One changes that relationship. The customer consumes capacity as a service. Everpure owns the burden of the underlying hardware lifecycle, the media strategy, and the ongoing optimization. DirectFlash makes that model stronger because the platform is structurally more efficient with the silicon it uses. It needs less trapped DRAM, wastes fewer writes, extends media life, and supports denser modules that deliver more usable capacity per unit of power, space, and raw media. Purity compounds that advantage with data reduction, ongoing software improvements, and smarter system-wide media management. Put differently, Everpure is in a much better position to absorb and manage component volatility than a customer buying boxes on a refresh schedule. That is the real price protection story. Not some magical promise that economics no longer apply. They do. NAND still costs what NAND costs. DRAM still costs what DRAM costs. Physics remains annoyingly undefeated. The difference is who is exposed to that volatility, how much inefficiency is built into the system before the customer ever sees it, and whether the operating model gives the customer a stable runway instead of a quarterly surprise. DirectFlash reduces the waste. Evergreen removes the forced disruption. Evergreen//One shifts the risk. That combination is a lot more interesting than it sounded in that room 11 years ago. The Part I Didn’t Appreciate Then What I did not understand sitting in that room 11 years ago was that some decisions are made for futures that have not arrived yet. The market eventually caught up to the architecture. That does not happen often enough in enterprise tech to ignore when it does. DirectFlash was never interesting just because it was different. It was interesting because it removed layers of inherited inefficiency that the rest of the market had accepted as normal. And in a period where NAND and DRAM pricing are under pressure, removing inefficiency is no longer just a performance story. It is a protection story. That is why this matters now. Not because it makes for a clever slide. Because it gives customers a more predictable way forward when the underlying component markets are anything but predictable. And in the current environment, that might be the most practical definition of innovation there is. Appreciate you reading. Dmitry Gorbatov © 2025 Dmitry Gorbatov | #dmitrywashereAI & Data Discovery
Would love your take on this GovLoop piece from my colleague, JimWeaver​ (former WA and NC CIO, ran NASCIO). One line that really stuck out to me: in a legislative hearing nobody asks if the model works. They ask where the data came from and who touched it. His first AI move in North Carolina wasn't buying a model. Instead, it was about data discovery. What do we have, where does it live, how clean is it. Has anyone here done similar data discovery efforts? How long did it actually take, and what surprised you? https://www.govloop.com/community/blog/building-an-ai-ready-data-foundation-for-state-and-local-government/Calling all public servants!
Did you know Evepure has a Public Sector community? Come talk shop with fellow federal, state and local govies, education leaders, and partners who get it. We're talking long procurement and CR-driven buying cycles, figuring out where AI actually fits when your data is spread across different systems, all amid election cycles and shifting political priorities. purecommunity.purestorage.com/group/public-sector If you're a customer or partner, would love to see you there! Leave a comment if you're interested and we'll get you set up.
GA: What's new in Purity//FA 6.10.5
We're excited to announce that the latest Purity//FA release is now GA! With the latest Purity//FA 6.10.5 release, customers get a tighter combination of performance, protection, and access control across their FlashArray estate. By combining ActiveCluster™ with near‑synchronous ActiveDR™, the platform now delivers a three‑site resilience capability that can help meet compliance expectations. You get zero‑RPO protection between two primary sites with ActiveCluster, replicating to a third site with ActiveDR. It’s engineered to deliver sub‑30‑second RPO and sub‑minute RTO. At the same time, support for FlashArray//ST™ R5 is here as part of the FlashArray family. It’s optimized to deliver consistent ultra low‑latency performance for OLTP, in‑memory, and real‑time workloads. Take advantage of extreme performance while maintaining consistent enterprise-grade data services. Support for NVMe/TCP with VMware 9.x and ActiveCluster means the most demanding VMware workloads don’t have to choose between performance and resilience. ActiveCluster provides zero-RPO protection, automatic failover, and active-active access for stretched datastores across NVMe/TCP. Rounding it out, Everpure Fusion now supports SAML 2.0 SSO, giving you a faster, simpler, and secure authentication experience across the Everpure Platform. Replace legacy local/LDAP logins with MFA‑backed SSO and IP‑based controls that harden your perimeter without adding operational friction while accessing FlashArray, FlashBlade, and Everpure Fusion fleets. Update to Purity//FlashArray 6.10.5 to leverage these latest innovations. Want to learn what else is new? Check out the Everpure What's New webpage today!
Snapshot protection
Hi guys, i'm trying to implement some kind of snapshot protection throughout all of our arrays. I have enabled Fusion. We're currently protecting the data with a 3rd party backup solution. I'm wondering how much of an impact to performance and space is enabled snapshots going to cause?. Since we're already protected, we're not looking into a very long retention with these backups (probably a week or so). I'm open to recommendations around this topic. Thanks!What’s getting in the way of AI moving into production?
Hi everyone! A lot of companies are experimenting with AI. Fewer are getting those projects into production. This Data Wire article looks at what can slow that down in financial services, including data readiness, governance, orchestration, and resilience. What are you seeing? What’s the biggest hurdle between an AI pilot and production? Would love to hear what others are seeing in the field.
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