Waddle on, federal friends. đ§
We know the rush of federal busy season is behind us, but the work doesnât stop. My kids gave me this little toy from their school's "Friday Shop" and it made me smile. Hope this spreads some encouragement for the week ahead. How are our federal folks doing? Drop a comment below. đ
Beyond Awareness: Awareness Won't Save You, Recovery Will
October 15 | Register Now AI has compressed breakout times to minutes, exposing the gap between knowing you're at risk and recovering from it. Rick Orloff, CISO, and Chris Brandt, Consulting Field Solutions Architect, show why resilience, not awareness, decides survival, and how to build the active-defender architecture that closes the gap. Weâll cover: Worse than expected â how the AI threat has grown dramatically in 2026. Backup vs. cyber recovery, and why you need a tested "minimum viable business" plan. How storage becomes an active defender: built-in security, connected detection, dynamic recovery. A real Fortune 100 recovery story showing resilience in action when prevention fails. Register Now!
Cooking with âEnterprise Data Cloudâ: Recipes for your Datacenter
September 24 | Register Now! Looking for practical ways to streamline your datacenter or hybrid cloud environment and run it more reliably? Following up from last Septemberâs episode, âCloud - A Place or a Strategy? Unpacking the Everpure Enterprise Data Cloudâ (EDC) with JD Wallace, host Andrew Miller invites Michael Sasse (Principal Technology Strategist with Everpure for 11 years) to discuss how to make EDC real for YOU in the midst of the many industry changes from even just 12 months ago. Weâll explore: Changing customer needs: What Michael has heard as he discusses EDC with customers, including how speed and efficiency matters more than ever and the risk of being replaced if youâre perceived as the âdepartment of noâ. What EDC is: If you love dogs, youâll love this (join us to see why). Weâll look at how EDC is driving the simplicity that FlashArray⢠brought to the storage industry throughout the datacenter and cloud. Cooking the EDC souffle (making It real): Weâll look at the building blocks of EDC from a technical perspective â things like Fleets, Topology Groups, Presets, observability, governance, automation and more. Of course, underpinning all of this is Evergreen//Oneâ˘. Presenting to the (exec) table: Think of this as delivering the meal. Having reviewed the building blocks, weâll look at the outcomes both you and your executives care about. Think of this as 1) who is leveraging EDC, 2) how has it helped (measurable outcomes), and 3) why does it matter? Even better, weâll discuss the EDC Blueprint: how Everpure is linking our products and features to outcomes that your CIO and CTO care about. September 24 | Register Now!Accelerate 2026 - Part 2 - The Light Switch Test
Earlier, in Part 1, I wrote that the Everpure Accelerate 2026 opening keynote did not really feel like a storage keynote. My takeaway from day one was simple: Everyone wants your data. The bigger question is who owns the context. Day two answered a different question. If day one was about why the Enterprise Data Cloud matters, day two was about how customers are supposed to get there without turning it into another giant transformation project that sounds great on stage or in a boardroom and then dies somewhere between budget approval, staffing constraints, internal politics, and the next urgent outage. That is why the second keynote mattered. It was not trying to restart the vision. The vision had already been established. It was about turning that vision into something customers could actually use: a methodology, a blueprint, and a way to connect data architecture to risk reduction, efficiency, agility, modernization, and business outcomes. And then John Colgrove, Coz, did what Coz does. He simplified the whole thing. Not by making it smaller. By making it clearer. The phrase that stayed with me from his session was not a technical phrase. It was not Enterprise Data Cloud, Data Primacy, Fusion, data intelligence, or workload mobility, even though all of those ideas were underneath what he was saying. It was the light switch. Coz talked about walking into a room at home and turning on the light. You know exactly what is going to happen. It is simple. It is obvious. It works the way you expect it to work. Then he compared that to walking into a conference room at the office, where five people spend the first few minutes trying to figure out how to turn on the right lights, dim the screen area, wake up the display, connect the laptop, and make the audio work. Everyone has lived that moment. It is also a perfect way to explain what Everpure has been trying to do since the beginning. Make the complicated thing feel like the light switch. That may sound too simple for enterprise infrastructure, but I think it is exactly the point. The best infrastructure does not feel simple because the problem is simple. It feels simple because somebody did the hard engineering work to hide complexity without hiding control. That has always been part of the Everpure story. When Pure Storage first became known in the market, the message was not only flash performance. Performance mattered, of course. But the thing customers really felt was that the experience was different. The arrays were simpler. The upgrades were non-disruptive. The support model was different. Evergreen architecture was different. The idea that you could keep modernizing without the usual forklift pain was different. Over time, that simplicity moved from one array to more of the environment. Fusion extended the idea from a single system to a fleet. Policy, placement, automation, workload mobility, service levels, compliance, and lifecycle management started to move from device-by-device thinking toward something broader. Now, with the Enterprise Data Cloud, Everpure is trying to move that simplicity again. From array to fleet. From fleet to data. From data storage to data management. That was the thread both Nirav Sheth and Coz pulled through the keynote, and I think it connected day two back to day one in a very useful way. They made it clear that the move from Pure Storage to Everpure is not an abandonment of what got the company here. It is a continuation of the same journey. That matters because customers are rightfully skeptical when technology companies rebrand or expand their message. They wonder whether the company is moving away from the thing they trusted. They wonder whether the new story is strategy or just vocabulary. Coz addressed that directly. We are not abandoning storage infrastructure. We are going to keep building the best storage infrastructure we can. But we are also going higher, because to build better infrastructure, you have to understand more about the data above it. That is a founderâs version of the message. Less theater. More first principles. If you store data, you want to know what it is. You want to know how it will be accessed. You want to know how often. You want to know what it relates to. You want to know whether there are copies. You want to know whether those copies create risk. You want to know whether the rules are being followed. The problem, as Coz pointed out, is that nobody really knows the future. The infrastructure has to be built for agility. That word gets overused, but in this context it matters. Agility is the ability to change without breaking everything. It is the ability to move workloads non-disruptively. It is the ability to rebalance a fleet. It is the ability to modernize hardware without turning it into a migration event. It is the ability to adjust policies as risk changes. It is the ability to bring intelligence to data that already exists instead of forcing the business to start over. That is where the Enterprise Data Cloud story becomes more practical. And I personally think the Enterprise Data Cloud Success Blueprint was the clearest example of that. I liked this part because it moved the conversation away from âlook at all these capabilitiesâ and toward âhere is why it matters to youâ and âwhat outcomes are you trying to drive?â That is where a lot of technology conversations go wrong. We get excited about the architecture and forget that customers are not buying architecture for the sake of architecture. They are trying to solve business problems with limited people, limited time, limited budget, and increasing pressure from every direction. They are dealing with supply chain constraints. They are being asked to do more with the same team. They are trying to create VMware optionality without making a reckless move. They are modernizing applications while still running legacy workloads that cannot just disappear. They are dealing with cyber risk, ransomware, and minimum viable business recovery. They are being asked to support AI before the data foundation is ready. The blueprint framework organized those pressures into three simple categories: risk reduction, efficiency, and agility. That may seem obvious, but obvious is underrated. Risk reduction is not just a security feature. It is knowing whether your data is protected, whether your snapshot policies are aligned, whether you can recover the minimum viable business, whether sensitive data is duplicated everywhere, and whether compliance follows the data instead of living in someoneâs spreadsheet. Efficiency is not just a density number. It is energy efficiency, automation, operational scale, fewer manual tasks, fewer migrations, and fewer people spending nights and weekends babysitting infrastructure that should be managing itself. Agility is not just modernization language. It is VMware optionality, container readiness, AI readiness, cloud flexibility, application mobility, and the freedom to make the next decision without being trapped by the last one. I think that is a much better way to have the conversation with customers. Not âDo you want this product?â But âWhich business outcome are you trying to improve, and what is standing in the way?â The Red Hat and CSX discussion made that practical. When Eric Grabill from CSX talked about Positive Train Control, sensors along the tracks, safety requirements, and systems where a loss of data can affect train operations, the conversation moved from platform strategy into the real world. That is where infrastructure earns its keep. CSX has already moved a large portion of its applications to Kubernetes on OpenShift, but still has legacy VMs remaining. That is the real enterprise pattern. It is not containers or VMs. It is containers and VMs. It is cloud and on-premises. It is modern and legacy. It is AI coming next while everything else still has to run today. The Red Hat and Portworx conversation made the point that modernization cannot mean creating another disconnected stack. Customers need one operating model across VMs, containers, and eventually AI workloads. They need a practical transition path, not a big bang migration. They need data services that protect the applications, not just compute platforms that can host them. The St. Elizabeth Healthcare conversation made the same point in a more personal way. Charles Shepherd talked about joining St. Elizabeth in 1997, starting at the help desk, moving through Novell, GroupWise, backups, storage, and eventually becoming part of the team responsible for systems that support a healthcare environment that never really stops. What stayed with me was not only the technical story. It was the laptop on vacation. Anyone who has worked in infrastructure understands that detail. The laptop that comes with you just in case. The phone you keep checking because maybe something happened. The family event where part of your brain is still in the data center. The trip where you are physically present but operationally on standby. That is not a feature comparison. That is a life comparison. Charles said he recently was able to go to his nieceâs graduation and not get called. That sounds small only if you have never been the person who always gets called. He also talked about more than one hundred hardware upgrades and more than one hundred fifty Purity upgrades without downtime. He talked about moving from older systems to modern ones without the traditional forklift migration pain. He talked about change boards becoming comfortable with upgrades during the day because the process had earned trust. That is the kind of customer proof that matters. It shows what the solution that was delivered gives back. It gives back time, trust and confidence. That connects directly to the light switch idea. Simplicity is not cosmetic. It is not just a better UI. It is not just fewer clicks. Simplicity changes what people can spend their time on. It changes what teams believe they can safely do. And it changes whether the infrastructure team is trapped maintaining the past or free to prepare for what comes next. Coz also said something important about time. This Enterprise Data Cloud journey is not a one-year story. It is not one product cycle. It is not done because it showed up in an Accelerate keynote. Coz described it as a journey that will take five to ten years, and even then, it will not really be done because the solution will keep improving. I appreciate that kind of honesty. So when a founder says this is a long journey, I believe that more than I believe a slide that says âseamless transformationâ in large font. But I also think now is the right time for the journey to become possible. And Coz reminded us that the best version of this is not complexity with better branding. The best version is the light switch. Coz, in the most Coz way possible, reminded everyone that the goal is not to make enterprise infrastructure sound impressive. The goal is to make the hard things feel obvious. Like turning on the lights. I appreciate you reading. Dmitry Gorbatov Š 2025 Dmitry Gorbatov | #dmitrywashereAccelerate 2026 - Part 1 - Everyone Wants Your Data
Back in February, I wrote that I had never been to Pure//Accelerate. This year, I still am not in Las Vegas. I watched the Everpure Accelerate 2026 opening keynote live from my home office, which turned out to be a different kind of vantage point. Not better than being there. Not worse. Just different. You do not get the hallway energy. You do not get the sponsor booths. You do not get the accidental conversations that happen while looking for coffee (or tea in my case). You do not get to read the room in the same way. You are not surrounded by the noise, the music, the badge lanyards, the customer reactions, or that strange conference feeling where everyone is tired and energized at the same time. A keynote viewed remotely has to work harder because the production does not carry you in the same way. If the speakers are just reading slides, you feel it immediately. If the story is thin, the distance makes it thinner. If the message is only a collection of announcements, you start checking email. But if the story is real, the distance does something interesting. It removes some of the theater. You are left with the words, the pacing, the ideas, and whether the people on stage actually believe what they are saying. This morning, the parts that landed with me were not the parts that felt the most polished. They were the parts where the keynote stopped sounding like a keynote and started sounding like people trying to explain a real shift in the industry. For me, the strongest moments came from Charlie Giancarlo, Chadd Kenney, Shawn Rosemarin, and the NVIDIA conversation. Not because they had the most slides. Not because they had the most numbers. They landed because they are storytellers. That matters more than we sometimes admit in enterprise technology. A slide reader can tell you what a product does. A storyteller can tell you why the product had to exist and why it matters to the customer or partner. There is a difference. And this morning, the difference mattered. Charlieâs most important line came early, and it changed the shape of the keynote. He said he was not going to talk about data storage. He was going to talk about data. For a storage companyâs major customer event, that is not a small pivot. It would have been easy for Everpure to spend the morning celebrating the familiar things. Growth. Customer count. Market share. Flash leadership. Subscription run rate. Fusion adoption. Gartner recognition. Net Promoter Score. Performance numbers. Efficiency numbers. All of that was there, and all of that matters. But the real keynote was not about proving that Everpure is good at storage. The real keynote was about arguing that storage is no longer the highest-level conversation. Data is. That is where the rebrand from Pure Storage to Everpure starts to become more than a name change. A company called Pure Storage can be excellent at arrays, controllers, upgrades, density, performance, and simplicity. A company called Everpure has to earn the right to talk about the enterprise data cloud, and that is a much larger promise. It is also a much riskier promise. Because once you move north of storage, you are no longer talking only about where data lives. You are talking about who controls it, who understands it, who governs it, who protects it, and who gets to use it. That is where Charlieâs message became interesting. His argument was that the enterprise has spent decades living in an application-centric architecture. ERP had one version of the business. CRM had another. ServiceNow had another. HR systems had another. Analytics platforms copied data out of all of them. Data lakes were built to make sense of the mess. Now AI agents are being asked to reason across that same fragmented landscape. That is the problem. Not AI in theory. AI on top of fragmented truth. We have all seen some version of this. The definition of a customer changes depending on which system you ask. The data in one application does not quite match the data in another. A report is technically correct, but only according to one source. A copy was made for analytics, another for backup, another for a project, another for a data science team, and one more because someone needed it urgently two years ago and nobody knows if it is still being used. Now imagine asking an AI agent to act on that. That is where the phrase âData Primacyâ becomes more than keynote language. From what I understood this morning, Data Primacy is Everpureâs argument that data should no longer be trapped inside applications as a secondary object. The data itself, along with its context, relationships, governance, and sources of truth, has to become primary. Applications still matter. Workflows still matter. SaaS still matters. But the enterprise cannot keep allowing every application to define its own version of reality and then expect AI to make intelligent decisions across the pile. That is not sustainable. One of the strongest lines from Charlie was the idea that every vendor wants your data. Every SaaS vendor wants your data. Every analytics vendor wants your data. Every AI vendor wants your data. But what they really want is not just the data. They want the context. They want the meaning. They want to know how a customer in one system relates to a contract in another, a support case in another, an invoice in another, a shipment in another, a security policy in another, and a business outcome somewhere else entirely. In the AI era, context is the new land grab. That is the sentence I kept coming back to during the keynote. Everyone wants your data, but the bigger fight is over who owns the context. That is why this morning did not feel like just another AI infrastructure pitch. It felt like Everpure trying to move the conversation from managing arrays to managing truth. That is a big claim. It needs proof. And that is where Chadd Kenneyâs part of the keynote mattered. Chadd is one of those speakers who can make infrastructure feel like an actual story instead of a list of capabilities. That is not easy. Storage features can get very technical very quickly, and if you are not careful, everything turns into a blur of replication, snapshots, policies, performance, controllers, and acronyms. But Chadd framed the platform in a way that made sense. First, the unified data plane stores the data. Then the intelligent control plane governs and operates it. Then the universal data intelligence layer helps understand it. Together, those pieces create a different operating model. That phrase, operating model, is important. Because the most interesting part of the demo was not just that Fusion can automate tasks. It was that Everpure is trying to change the relationship between people and infrastructure. The old world asks humans to be perfect by hand. Log into this array. Check that policy. Fix that snapshot setting. Compare it to the spreadsheet. Make sure the production workloads are covered. Exclude dev and test. Check compliance. Update the retention period. Open the change. Wait for the window. Hope nobody missed one. We have normalized that kind of work for years. Then we call it operational discipline. Sometimes it is. But sometimes it is just human beings being forced to compensate for systems that do not understand intent. The Fusion demos showed something better. Define the policy once. Attach intent to the workload. Let the control plane detect drift. Let it show violations. Let it recommend a fix. In some cases, let it act. In other cases, keep the human in the loop, but stop making the human do repetitive work that the platform should be able to understand. The ransomware snapshot example was a perfect illustration. A customer mandate changes retention from fifteen days to thirty days. In many environments, that becomes a manual chase across infrastructure. In the keynote demo, it became a policy problem, a compliance view, a remediation path, and an audit trail. That is what infrastructure teams actually need. Not another dashboard that tells them something is broken. A system that understands what âcorrectâ looks like and helps keep the environment there. The workload mobility demo made the same point from a performance angle. If the platform can see that a workload is trending toward a service level violation, recommend a better placement, validate the move, and relocate that workload without the application owner noticing, that is not just automation. That is infrastructure keeping a promise quietly. And quiet matters. The best infrastructure usually disappears. Nobody sends a thank-you note because latency did not spike. Nobody opens a champagne bottle because a replication policy worked. Nobody celebrates the outage that did not happen. But those invisible wins are the difference between a team that spends its life firefighting and a team that gets to work on the next thing. That is why the Active Cluster for File demo also worked. Synchronous replication for file sounds technical, and it is. But the emotional value is simple. When something fails, the business does not want a heroic recovery story. It wants no story at all. The workload quietly packed its bags and moved. That line stayed with me because disaster recovery should be boring. Boring is the goal. Boring means the policy worked, the automation worked, the architecture worked, and the humans did not have to assemble on a bridge call at 2 a.m. to save the day. Then Shawn Rosemarin took the keynote into the AI conversation, and again, the strength was in the framing. The bottleneck stalling AI is not compute. It is not models. It is not tooling. It is data. That is the part of the AI conversation that I think many customers are starting to feel more clearly. For the last few years, the market has been obsessed with models and GPUs. That made sense. There was a lot to understand, and the infrastructure requirements are real. But most customers are not trying to build the next frontier model. They are trying to unlock the intelligence that already exists inside their own business. Their documents. Their contracts. Their transactions. Their support history. Their clinical records. Their policies. Their engineering files. Their internal knowledge. Their institutional memory. That does not become useful just because someone points a model at it. It becomes useful when the data is prepared, classified, curated, governed, indexed, vectorized, and delivered with the right context at the right time. That is why the Everpure Data Stream announcement with NVIDIA mattered. The message was not simply, âWe can feed GPUs fast,â although performance absolutely matters. The more important message was that enterprise AI needs a way to make data AI-ready without creating another silo, another copy, another stale version of the truth. Shawn made the point clearly: other vendors want you to copy your data into their system. But a copy is always behind. That is such a simple sentence, but it carries a lot of weight. A copy has to be protected. A copy has to be governed. A copy has to be reconciled. A copy has to be secured. A copy can drift. If AI is going to answer questions, automate workflows, make recommendations, or support decisions, stale context is not just inefficient. It is dangerous. That is where the NVIDIA conversation added credibility. The point was not just that Everpure has a partner logo on a slide. The point was that AI has become a full-stack infrastructure problem. Data has to move efficiently. GPUs cannot sit idle waiting for bytes. Inference needs low latency and quick access to the right information. Agents need context. Networking matters. Storage matters. Hardware matters again. I liked the line about hardware being cool again. I liked it because it is funny, but also because it is true. For years, parts of the industry talked as if infrastructure had become invisible. Cloud abstracted it. SaaS hid it. Software ate the world, and many people acted as if the physical layer was someone elseâs problem. AI ended that illusion. When customers are investing serious money in GPUs and trying to build real AI capability, storage is not a commodity. Networking is not plumbing. Metadata performance is not trivia. Power is not someone elseâs concern. Operational excellence is not optional. The physical world is back in the strategy conversation. Maybe it never left. Maybe some people just stopped looking. That is why the keynote worked for me, even from my home office. Not because every demo was relevant to every customer. Not because every phrase was perfect. Not because I think one keynote answers every question. That is what the next two days are for. It worked because the best speakers were not just announcing things. They were connecting the dots. Charlie explained why the application-centric model is running out of room. Chadd showed what it looks like when infrastructure starts operating from intent instead of manual heroics. Shawn connected AI success back to data readiness instead of model worship. The NVIDIA conversation reminded everyone that AI is not magic. It is infrastructure, data movement, context, governance, and execution. That is a story. And in enterprise technology, the story matters because customers are not just buying features. They are buying a way out of the mess they are already in. The mess is fragmentation. AI did not create that mess, but AI is making it impossible to ignore. That may be the real takeaway from day one of Accelerate. The AI era is forcing enterprises to confront the data architecture choices they have been living with for decades. The old model was manageable when humans were the ones reconciling the gaps. It becomes much more dangerous when agents begin acting on top of those gaps at machine speed. That is why Everpureâs move toward Data Primacy is worth paying attention to. It is not just a product direction. It is a point of view. The application should not own the truth. The copy should not become the truth. The dashboard should not pretend to be the truth. The enterprise needs to own its data, understand its context, govern its use, and make it available to applications, analytics, and AI in a way that is coherent and trustworthy. That is easy to say and very hard to do. But this morning, for the first time, I felt the full shape of what Everpure is trying to become after the rebrand. Not just a storage company with a new name. Not just a platform company using AI language because everyone has to. But a company trying to move the enterprise conversation north of storage and into the question that will define the next decade: Who owns the context of your business? Because everyone wants your data. The companies that win will be the ones that know what it means. I appreciate you reading. Dmitry Gorbatov Š 2025 Dmitry Gorbatov | #dmitrywashere
As-a-service consumption - Evading Market Volatility
NAND flash prices are up over 175% year-over-year. Quotes are expiring in two to four weeks. Contracts you thought were closed are getting repriced before the hardware ships. For finance leaders, this isn't just a procurement problem; it's a planning problem. Capital cycles built on stable pricing assumptions are getting stress-tested in real time. Join Everpure experts for a direct conversation on what the market is doing to infrastructure budgets, and how a consumption-based model stops the volatility before it reaches your P&L. Why join this session? This market shift isn't short-term, so what does it mean for multi-year budget planning Where traditional purchase models are breaking down, and the operational overhead of managing hardware you shouldn't have to think about How Evergreen//One⢠moves storage from a purchasing decision to a predictable service underpinned by SLAs Why this makes a compelling business case for your business - for your CFO, your board, or your next budget cycle Register Now
