Healthcare Sessions at Accelerate 2026
Accelerate is almost here! 🙌 Check out our special track tailored for healthcare professionals. Hear from Everpure experts about the latest trends and strategies for data management success in healthcare. If this sounds right up your alley, we’ve curated a list of must-attend breakout sessions focused on all things Healthcare. Check out these don't miss sessions: Modernizing Epic Infrastructure: Performance, Flexibility, Resilience, and Virtualization Evolving Everpure Cloud Value: A Smarter Hybrid Cloud Approach for Healthcare Predictable Imaging Economics: Powering End-to-End Solutions with Price-per-Study Edge AI in Production: Real-Time Decision-Making in Sanofi’s Cryo-EM Core Will you be joining us in person? Drop a comment below with the session you're most interested in and what you hope to learn!Financial Services Sessions at Accelerate 2026
Accelerate is almost here! 🙌 Check out our special track designed to connect with FSI professionals. Hear from Everpure experts about the latest trends and strategies for data management success in Financial Services. If this sounds right up your alley, we’ve curated a list of must-attend breakout sessions focused on all things Financial Services. Check out these don't miss sessions: Why 95% of AI Pilots Fail at Scale, and How a Finance Sector MSP Beat the Odds Sovereign by Design: Delivering a Cloud Operating Model within the Firm’s Own Borders Cyber Defense in a Flash: Spotting the Smoke Before the Fire Will you be joining us in person? Drop a comment below with the session you're most interested in and what you hope to learn!
New IDC Research: Is Your AI Stack Ready? IDC Settles the Debate
July 9 | Register Now More than half of enterprise AI projects never make it to production and new IDC research reveals the surprising reasons why. Join us to see the exclusive results from our joint global survey of 1,300 organizations. This session will uncover what separates AI leaders from those stuck in pilot purgatory, including critical findings about data security, GPU underutilization, and the hidden time tax that slows data scientists. See how your AI maturity stacks up against IT peers and learn the concrete steps that leading organizations are taking to move from experimentation to mastery. Key takeaways include: New research that uncovers what’s stalling enterprise AI initiatives Which infrastructure and data decisions actually predict AI project success How your organization benchmarks against the IDC AI Readiness Index What leading enterprises are doing differently to achieve ROI Register Now!Webinar: What It Takes to Build AI-Ready Telco Clouds
Shameless plug, Fierce Network hosted a webinar with Nokia, Everpure, and Red Hat. In this webinar, Nokia, Red Hat, and Everpure will share perspectives on the strategic considerations shaping next-generation telco cloud design—from standardization and lifecycle management to data readiness, operational efficiency, and support for distributed environments from core to edge. The session will explore how service providers can think about building a modern telco cloud foundation that is better prepared for automation, analytics, and long-term innovation. Check out the Webinar hereA Petaflop in Your Backpack: RTX Spark and the Shift to Local AI
Alt headlines to A/B test: The Cloud-Bill Killer? RTX Spark Puts a Full Petaflop of AI on Your Lap Local AI Just Got Serious: 128 GB and a Petaflop, Unplugged Stop Renting GPUs. RTX Spark Brings Data-Center AI to Your Desk — and Your Bag For the last few years, doing real AI work meant one thing: renting someone else's hardware. Spin up an instance, watch the meter run, ship your data to a server you don't control, and hope the latency cooperates. RTX Spark is a bet that the next era looks different — that the most interesting AI will run where you are, on hardware you own. And the spec sheet backs up the ambition. The numbers that matter to builders Up to 1 petaflop of FP4 AI performance Up to 128 GB of unified memory Up to 6,144 cores of Blackwell RTX GPU Up to 20 cores of ultra-efficient CPU If you build with AI, two of these should stop your scroll. A full petaflop of FP4 is the kind of throughput that turns "let it run overnight" into "let it run over lunch." And 128 GB of unified memory is the unlock almost no portable machine offers: CPU and GPU share one giant pool, so you can hold large models and big context entirely in memory — no swapping, no artificial caps on the size of what you load. The workloads that used to demand a rack now fit in a bag. Why this is a big deal for AI work CUDA runs natively. The platform that accelerates the world's AI runs at full speed on RTX Spark — meaning the frameworks, libraries, and agentic stacks you already use just work. No exotic ports, no "supported soon." That changes the day-to-day in three concrete ways: Fewer cloud bills. Prototype, fine-tune, and run inference locally instead of burning credits every time you iterate. Your data stays yours. Sensitive datasets never leave the device — a quiet superpower for anyone working under compliance or NDA. Build agents anywhere. A genuinely portable petaflop means you can develop and test agentic workflows on a train, in a client's office, or off the grid entirely. For the wave of teams building agents and AI products right now, "local-first" stops being a compromise and starts being an advantage. A petaflop that doesn't need a power brick Here's the part that makes the rest believable: RTX Spark is built around the most power-efficient RTX chip ever made. That efficiency is why a petaflop can live in a slim chassis and last all day. Performance you can only use while tethered to a wall isn't really portable performance — and that's the trap RTX Spark is designed to avoid. When the work is done It's not all inference and fine-tuning. The same silicon makes RTX Spark a creator's machine — hundreds of creative apps and AI tools accelerated by RTX and NVIDIA Studio — and a genuine gaming rig after hours, with ray tracing, the full DLSS suite, NVIDIA Reflex, and G-SYNC. One device, three lives. The takeaway The story of AI has been a story of renting access to power. RTX Spark points at a different future: owning it, carrying it, and pointing it at whatever you're building — without the meter running. So here's the real question If you had a portable petaflop with 128 GB of unified memory, what's the first thing you'd run on it — a local LLM, an agent swarm, your own fine-tune? Drop it in the comments. I'm genuinely curious what this community would build first.Artificial Intelligence (AI) Sessions at Accelerate 2026
Accelerate 2026 is right around the corner! 🚀 Learn how to build a blueprint for AI competitive advantage with the Everpure platform. Hear from industry experts and customers about best practices to create your AI factory—from pilot to production. If this sounds right up your alley, we’ve curated a list of must-attend breakout sessions focused on all things AI. Check out these don’t miss sessions: The State of AI, 2026: Top Advances and Their Impact on Global Enterprises Come See What We've Been Building: A Guided Tour of Everpure's Enterprise AI Portfolio Data Stream: Zero Friction from Storage to AI How are NeoClouds, Enterprises and Commercial Customers Adopting AI at Scale with Everpure 🎤 Featured Speaker We are thrilled to have Par Botes sharing deep-dive insights into the State of AI in 2026. You won't want to miss this keynote! Will you be joining us in person? Drop a comment below with the session you're most interested in and what you hope to learn!"Where’s Waldo?", But for your Data
This past Saturday, my wife and I sat at my son’s college graduation ceremony doing what every proud parent does after running out of tears and tissues: staring at the giant screen, scanning a crowd of thousands, and playing a very emotional, very expensive version of Where’s Waldo? The camera pulled back and showed the graduating class. Thousands of caps. Thousands of gowns. Thousands of people who had just survived exams, group projects, late-night studying, bad cafeteria decisions, emotional phone calls home, and whatever personal version of “I’ll start the paper tomorrow” they subscribed to. Somewhere in that sea of mostly identical academic robes was my son. I knew he was there. We had dropped him off at college years earlier, paid tuition, bought supplies, endured move-in day, survived the separation anxiety, worried about him, cheered for him, and occasionally pretended to be calmer than we actually were. I knew exactly why we were in that room. But on that screen, in that moment, he was just one face among thousands. So I started searching for him. Every parent around me was probably doing some version of the same thing. We were not looking at a graduating class in the abstract. We were looking for our graduate. Everyone else on that screen mattered deeply to someone, but to us they were mostly context without identity: a massive, moving, emotional dataset with almost no metadata attached. That was the strange thing about the picture. It showed us everything and told us almost nothing. There were thousands of people on the screen, but unless you already knew who you were looking for, you did not really know what you were looking at. Somewhere between the pride, the camera angle, and my increasingly poor performance at parental facial recognition, my brain did what my brain unfortunately does. It connected a very human moment to the way enterprises think about data. Because this is exactly the problem most organizations have with their data. They know it is there. They know there is a lot of it. They know some of it is incredibly valuable, some of it is probably risky, and some of it is duplicated, outdated, forgotten, regulated, misplaced, or being accessed by people and systems nobody has thought about in years. But knowing there is a crowd is not the same thing as knowing who is in it. That is the part we do not talk about enough. For years, data management conversations were mostly about where the data lived, how it was protected, how fast it could be accessed, and how much it cost to keep it all running. Those things still matter. They will always matter. But they are no longer enough. The new question is not simply, “Where is the data?” The better question is, “What is this data, who does it belong to, why does it exist, who is using it, where has it moved, what risk does it carry, and should this AI model, business process, analyst, application, or employee be touching it at all?” That is a very different conversation, and that is why 1touch matters. Not because the industry needed one more product logo, one more acronym, or one more keynote phrase that sounds important until everyone quietly admits they are not exactly sure what it means. 1touch matters because it is aimed directly at the problem of not knowing. The lie of visibility Most organizations believe they have visibility into their data because they have tools that can show them infrastructure. They can show arrays, volumes, file systems, buckets, databases, dashboards, latency charts, replication status, backup jobs, snapshots, anomalies, alerts, and the occasional red icon that ruins someone’s morning. All of that is useful. None of it guarantees understanding. An IT team can tell you a volume is 87 percent full, but that does not mean they know it contains expired customer records, old HR exports, forgotten underwriting files, production data copied into a test environment, or a spreadsheet with 40,000 Social Security numbers created in 2018 by someone who left the company three reorganizations ago. A security team can tell you an alert fired, but that does not mean they know whether it represents real exposure, a false positive, or just another noisy event in a pile nobody has enough hours to investigate. A data team can point to a lake, a warehouse, a catalog, and a governance process, but that does not mean the data is clean, trusted, current, properly classified, or safe to feed into an AI workflow. This is the uncomfortable truth: enterprise data visibility has often meant visibility into containers, not contents. We could see the auditorium. We could count the very uncomfortable seats. But we still could not tell which graduate was my son. The graduation screen was not useless. It showed scale. It proved the event was real. It helped me understand the crowd. But until I could identify the person I cared about, the picture was incomplete. Enterprise data estates work the same way. The problem is not that organizations have no tools. They often have too many. The problem is that many tools see the surface of the environment but miss the identity, relationship, movement, and meaning of the data inside it. That gap was inconvenient in the old world. In the AI world, it is dangerous. AI does not forgive ignorance Before generative AI entered every boardroom conversation, the consequences of not knowing your data were already serious: compliance exposure, bloated infrastructure costs, security blind spots, slow audits, manual discovery, painful legal requests, cloud migration delays, and business users waiting weeks for access to information because nobody could confidently say what was safe to use. Then AI showed up and made the problem louder. AI feeds on data. Lots of it. Structured data, unstructured data, documents, emails, transcripts, PDFs, customer records, logs, knowledge bases, support case histories, SaaS exports, file shares, objects, and anything else that might help a model answer a question, summarize a situation, automate a workflow, or make a decision. That sounds exciting until you remember that most enterprises do not fully know what is in all of those places. And AI is not magic. If the input is wrong, the output inherits that problem. Sometimes the model hallucinates. Sometimes it exposes something it should not. Sometimes it makes a recommendation based on data that was never supposed to leave a specific jurisdiction. Sometimes it answers confidently from a document that was obsolete three policies ago. Sometimes it gives the right answer to the wrong person, which may be the scariest version of all because the technology can look like it is working while quietly violating the trust model of the business. That is why “AI-ready data” cannot simply mean “we pointed a model at a repository.” That is not readiness. That is hope with an API call. AI-ready data needs context. It needs classification, identity, policy, and confidence. It needs a way to distinguish between a harmless document, a restricted record, a regulated attribute, an exposed credential, and a data fragment that only becomes sensitive when connected to other fragments somewhere else. A number or a name by itself may not mean much. A location, transaction, or timestamp by itself may not mean much either. But connect the number to the name, the name to the patient record, the patient record to a geography, the geography to a regulation, the regulation to a storage location, and the storage location to an access path, and suddenly you are not looking at random data anymore. You are looking at risk. Or value. Often both. This is where 1touch becomes important, because its value is not just identifying patterns and sticking labels on files. Its value is in discovering, classifying, and contextualizing data across environments so organizations can understand not only what exists, but what it means. That distinction matters. The difference between labeling and knowing At graduation, every student had the same basic label: graduate. That label was accurate, but it was wildly insufficient. One graduate may be heading to medical school. Another may be joining a startup. Another may be the first person in their family to earn a degree. Another may have worked two jobs to get there. Another may have changed majors three times and somehow still finished on time, which frankly deserves its own medal. The label tells you the category. The context tells you the story. Data works the same way. A traditional tool might identify something that looks like a credit card number, Social Security number, email address, medical code, account number, or passport field. That is useful, but it can also create noise. Strings of digits appear everywhere. Test data looks real. Real data looks fake. A file name can lie. A folder path can be misleading. A database column called “ID” might be harmless, or it might be the key to everything. Context is what turns a guess into intelligence. 1touch approaches this problem by looking at the broader semantic environment around the data. It is not just asking, “Does this pattern match something sensitive?” It is asking, “What surrounds it? What system did it come from? Who accesses it? Where does it move? What other data is connected to it? What business process does it support? What regulatory meaning does it carry?” That matters because in the real world, data risk rarely lives in a single isolated field. It lives in relationships. The same way my son was not immediately identifiable to the room because he was wearing a cap and gown like everyone else, sensitive enterprise data is often not obvious because it is dressed like everything else. It sits in file shares, databases, cloud repositories, SaaS platforms, mainframes, archives, exports, and forgotten project folders. It blends into the crowd. The old approach was to scan the crowd every so often and hope you recognized enough faces. The newer requirement is continuous understanding: discovering data where it lives, watching how it moves, connecting fragments across systems, and building a living map of identity, access, classification, and risk. Not a once-a-year inventory. Not a spreadsheet. Not a governance theater exercise where everyone nods in a meeting and then goes back to copying production data into development because the test system “needed something realistic.” A living map. That is the real promise. Why this matters The value of 1touch can be easy to undersell if we describe it only as sensitive data discovery or Data Security Posture Management (DSPM). Those descriptions may be accurate, but they are not the business problem. A prospect is not waking up hoping to buy a classification engine. They are waking up with pressure from the board, auditors, regulators, cyber insurers, application owners, AI initiatives, cloud migration teams, and business leaders who want faster access to “clean” data without increasing risk. And for those of us who have been around this industry long enough to have a few emotional support scars, this problem is not new. We were talking about lifecycle data management and data classification projects 20 years ago. Kazeon, StoredIQ, and others were all trying to help customers understand what was hiding inside their unstructured data environments before the phrase “dark data” became a fashionable way to describe a very unfashionable mess. I personally used Kazeon back in 2006, before EMC acquired it and eventually killed it. The idea was right. The experience was painful. I remember a project where it took almost two months to scan the environment, process the results, and prepare the report. We finally sat down with the customer, proudly showed them the findings from roughly 5TB of unstructured data, and waited for the moment where they would appreciate all the classification goodness we had brought into their lives. Instead, the customer looked at us and asked the only question that mattered: “Where is the rest of my 55TB?” There are moments in a technical meeting when the room temperature changes without the thermostat being involved. This was one of them. Apparently the tool did not have permissions to scan the rest of the environment. So after two months of work, the result was technically accurate and practically incomplete, which is the most dangerous kind of confidence. We had a report. We had charts. We had findings. What we did not have was the whole truth. That is why this matters now. The enterprise data problem did not begin with AI. AI simply made the consequences of incomplete understanding much harder to ignore. Twenty years ago, a bad classification project meant a frustrated customer, an awkward meeting, and a lot of manual cleanup. Today, the same kind of blind spot can contaminate an AI pipeline, expose regulated data, break a sovereignty policy, delay a migration, or give executives a false sense of security. For existing customers, the value is even more strategic. They already trust the platform to store, protect, move, and serve their data. The next logical question is whether it can help them understand the data as well. That is the bridge 1touch helps build. That is important because customers are tired of stitching together disconnected tools where one product sees storage, another sees identity, another sees security events, another sees data catalogs, another sees cloud posture, and another sees compliance workflows. Everyone sees something, but nobody sees enough. Customers do not need more fragmented visibility. They need connected context. Most importantly, it helps us explain why the conversation has moved from where data sits to what the data actually means. Back to the screen Eventually, during the ceremony, I found my son. Definitely when his name was announced and he walked across that stage. But the moment stayed with me because it was such a simple reminder: seeing a crowd is not the same as knowing the people in it. Every person on that screen had a story, a history, a family somewhere in the stands trying to yell the loudest, and a future that was about to begin. From a distance, they looked identical. Up close, they were anything but. Enterprise data is like that too. From a dashboard, it can look like capacity, files, objects, tables, volumes, buckets, repositories, shares, records, and logs. But inside that data are customer identities, patient histories, citizens tax records, contracts, intellectual property, employee information, business secrets, stale copies, duplicate exports, forgotten archives, useful insights, hidden risks, and the raw material for the next generation of AI-driven business processes. The organizations that win will not be the ones that simply store the most data. They will be the ones that know what their data means. That is why 1touch matters. Because the future of data management is not just finding Waldo. It is understanding the entire crowd. Appreciate you reading. Dmitry Gorbatov © 2025 Dmitry Gorbatov | #dmitrywasherePart 2: MCP Is Interesting. Everpure Fusion Makes It Useful.
In Part 1, I tried to give MCP a proper “…splanation,” mostly because the first several times I heard people talking about Model Context Protocol, I had the same look Joey had in Friends when the salesman asked him if his friends ever had a conversation and he just nodded along without really knowing what they were talking about. That was me. MCP this. MCP server that. Agentic AI. Tool calling. Context windows. Protocols. Hosts. Clients. Servers. At some point, I realized I was nodding with the confidence of a man who had understood approximately 41% of the conversation and was hoping nobody asked a follow-up question. The simple version is this: MCP is a standard way for AI applications to connect to tools and data. It is not the AI model itself. It is not the magic brain. It is the plumbing that lets the AI reach into approved systems, ask better questions, retrieve useful context, and potentially take action through well-defined tools. That is important in the abstract. But for Everpure customers and prospects, it becomes much more interesting when we stop talking about MCP as a general AI concept and start talking about what it could mean for storage operations, data infrastructure, and Everpure Fusion. Because this is where the conversation moves from “AI is coming someday” to “your infrastructure may already need to be ready for how AI will interact with it.” Everpure recently published a blog with a sneak peek of the Everpure Fusion MCP Server, describing it as an open-source service that connects AI assistants to Everpure Fusion storage fleets through the Model Context Protocol. The important part is not simply that an AI assistant can talk to storage. That would be interesting, but it would also be easy to misunderstand. The important part is that the assistant can interact with the storage environment through the Fusion control plane, which already understands fleet-wide context across FlashArray and FlashBlade. That distinction matters. Without Fusion, many environments are still managed in a way that looks very familiar to anyone who has spent time supporting infrastructure. One array over here. Another array over there. Scripts in one folder. Notes in another. Naming standards that started strong and then apparently met reality. Screenshots in tickets. Tribal knowledge in the heads of a few people who somehow remember which workload lives where, which array is doing what, and why nobody should touch that one volume because “there was a reason,” even if nobody is entirely sure what the reason was anymore. That model may work, but it does not scale gracefully. More importantly, it is not especially friendly to automation, and it is definitely not ideal for AI-assisted operations. Most troubleshooting in mature environments is not hard because people lack tools. It is hard because the context is not immediately obvious. The storage admin has one view. The DBA has another view. The virtualization team has another view. The application owner has a completely different view, usually delivered through a ticket that says something deeply scientific like “the app feels slow.” Everyone may be looking at a valid piece of the puzzle, but the real work is in the correlation. Which volume maps to which workload? Which array is hosting it? What did latency look like during the reported window? Were IOPS elevated? Was bandwidth constrained? Did anything change recently? Are we looking at a storage issue, a database issue, an application issue, a noisy neighbor, a misconfigured VM, a bad query, or just another case of “the network is innocent until proven guilty, but still somehow looks suspicious standing there”? That is where Fusion and MCP together become compelling. The Everpure Fusion MCP example makes the idea real. Instead of forcing an administrator to manually build low-level REST API calls or jump between tools, the MCP-aware AI assistant can query Fusion through higher-level tools exposed by the MCP server. In the example Everpure blog described, a storage admin can ask about workloads and volumes supporting a production SQL environment, including arrays, IOPS, latency, and bandwidth over a recent time window. The assistant can then correlate that storage perspective with information from another MCP server, such as SQL Server context around database files, wait types, and query behavior. That does not mean the AI replaces the storage admin. It does not mean the AI replaces the DBA. It does not mean everyone goes to lunch while the robot fixes production. And this is where I need to bring in The Big Bang Theory again, because apparently this is who I am now. There is a scene in the show where Raj is very open to the idea of aliens and extraterrestrial life. At the planetarium, Raj can look at flashes of light in the sky and talk about how scientists cannot fully rule out the possibility of alien civilizations. It is funny because Raj is a scientist, but he is also Raj, so the line between rigorous possibility and “maybe the aliens are waving at us” gets wonderfully blurry. That is how some people talk about AI operations right now. A light flashes in the sky, and suddenly someone is ready to announce that the robots are here to run the data center. Let’s not do that. The point is not that the AI is an alien civilization arriving to take over infrastructure operations. The point is that the interface is changing. The way humans interact with infrastructure is starting to move from manual lookup, command execution, and tribal knowledge toward assisted reasoning, guided action, and cross-system correlation. That is much more practical than aliens. It is also much more useful. Fusion already gives customers a fleet-wide control plane. It gives you the ability to think above individual arrays, above one-off configuration, and above the old habit of managing infrastructure like every system is its own little island with its own weather pattern. MCP gives that control plane another interface, one designed for the way AI agents work. This is why Fusion adoption matters. If your environment is still managed mostly array by array, script by script, ticket by ticket, and screenshot by screenshot, then AI can only help so much. It may summarize the pain beautifully, but it is still summarizing pain. When you use Fusion to create a more consistent, policy-driven, fleet-aware operating model, you are not just modernizing storage management. You are making the environment more understandable to automation, to operations teams, and now to AI agents that need structured context in order to be useful. That is a very different conversation from “look, the AI can query storage.” The better conversation is this: if AI is going to become part of operational workflows, then your infrastructure needs to be ready to participate in those workflows. Fusion is one of the ways you prepare for that. Not someday. Now. And Fusion is not the only example of this direction. Another Everpure technical article shows how an MCP server can be built to integrate with FlashBlade, allowing an AI assistant to query system data and even take direct actions through a natural-language interface. That example is useful because it shows the bridge between the old world and the new one. In the old world, storage management often meant CLI commands, scripts, API calls, screenshots, and specialized knowledge living in the heads of a few very tired people. In the new world, those capabilities can be surfaced through an AI-assisted experience that understands the available tools and can help operators ask better questions in plain English. Again, that does not mean the AI should blindly run your infrastructure while everyone disappears. Please do not read this article and tell your change advisory board that “the blog guy said the robot can handle it.” That is not the point, and I would like to remain welcome in polite infrastructure society. The point is that the operational model is changing. For years, we have talked about automation in infrastructure, but a lot of what we called automation still required a human to know exactly what to automate, where to look, which command to run, which script was safe, which API endpoint mattered, and which piece of documentation had not quietly aged into fiction. AI-assisted operations changes the interaction pattern. Instead of always beginning with the operator knowing the exact command or API call, the operator can begin with the question. Why did this workload slow down? Which volumes support this application? What changed in the last four hours? Which arrays are carrying the highest latency? Which workloads are consuming the most bandwidth? Which policies are inconsistent across the fleet? Where do we have capacity pressure? Which storage objects are tied to this SQL environment? Those are the kinds of questions humans actually ask when something is happening. MCP gives AI assistants a standard way to ask approved systems for the data behind those questions. Fusion gives the storage estate a more consistent, policy-aware, fleet-level way to answer. That combination is where the opportunity lives. Now, because this is enterprise technology and not a children’s book, we also need to talk about the dangerous part. One of the readers posted this comment on Linked in yesterday: The moment an AI system can access tools and data, the conversation changes. A chatbot that gives a bad answer is annoying. An agent that takes the wrong action in a business system can become a real incident. If a model can read sensitive files, query databases, send messages, modify records, trigger workflows, or touch infrastructure, then security is not a feature. Security is the premise. This is where some of the MCP enthusiasm needs adult supervision. We have spent years telling users not to click strange links, not to approve unknown applications, not to reuse passwords, and not to download random files. Now we are building systems where an AI assistant might read strange content, call external tools, and act on behalf of the user. That can be incredibly powerful, but only if we are honest about the risk. In some ways, MCP may expose organizational problems faster. If your data is scattered, stale, contradictory, or politically curated, an AI agent connected to it will not magically produce truth. It may simply produce a more polished version of the confusion. If your workflows are unclear, connecting AI to them may help automate the ambiguity, which is not quite the same thing as progress. The model can gather information, call tools, and complete steps, but people still need to define what should happen, what should not happen, what requires approval, and what good looks like. For Everpure customers and prospects, the more important question is not whether MCP is interesting. It is whether your environment is ready for this kind of interaction. That is where I would encourage customers to take a serious look at Fusion. Not because Fusion is another checkbox on a feature list, and not because every new technology conversation needs to end with someone saying “platform” three times into a mirror. Fusion matters because it changes the operational model. It gives you a way to manage data infrastructure as a fleet, with policy, consistency, automation, and context. Those are exactly the things AI agents need if they are going to do more than produce nicely formatted guesses. If you already met all the prerequisites (Purity 6.8.+, LDAP enabled), use it. Explore it. Get comfortable with it. Stop thinking about Fusion as something reserved for a future automation project after everyone finally gets through the current list of fires, renewals, upgrades, and meetings that should have been emails. MCP may be the plumbing that helps AI connect to the enterprise. Fusion helps make the storage environment worth connecting to. And that is the real call to action. Fusion is how Everpure customers make sure their data infrastructure is ready for it. Appreciate you reading. Dmitry Gorbatov © 2025 Dmitry Gorbatov | #dmitrywashereMCP, Joey Tribbiani, and the Moment AI Needed Plumbing - Part 1
People close to me know that I have a very annoying habit of memorizing, remembering, and using movie and TV show lines in normal conversation. I wish I could tell you this is a carefully curated personality trait, but it is probably closer to a long-running defect in the #dmitrywashere operating system. Some people remember birthdays. Some people remember where they parked. I remember a line from a sitcom episode that aired before half the people reading this had a LinkedIn profile. My two favorite sources are Friends and The Big Bang Theory, which probably says something about me that I am not emotionally prepared to unpack in public. There is a scene from Friends that has lived rent-free in my head for years, mostly because it captures something deeply human and mildly embarrassing. A salesman is talking to Joey and asks him a question that is both funny and a little too accurate: “Let me ask you one question. Do your friends ever have a conversation and you just nod along even though you’re not really sure what they’re talking about?” Joey, of course, immediately zones out. Not metaphorically. Not politely. He disappears into that wonderful Joey place where the mouth stays closed, the face stays agreeable, and the brain has clearly left the building. That was me the first few times I started hearing people talk about MCP. Not once. Not twice. Everywhere. MCP this. MCP server that. MCP is the future of agents. MCP is the USB-C of AI. MCP is how models connect to tools. MCP is the protocol that will make agentic AI real. MCP is the standard. MCP is the integration layer. MCP is the thing everyone apparently understood already, except somehow nobody had bothered to send me the memo. So I did what any responsible technology professional does in that situation. I nodded thoughtfully. The next thing I did was call my son, who is a Data Scientist, and ask him what MCP actually was. After listening to his explanation, I had the uncomfortable realization that he knew more about it than I did, which, naturally, did not feel great. That was just my ego talking, of course. He is way smarter than me. Then I went away and tried to figure out whether MCP was actually important or whether it was just another acronym that had wandered into the AI conversation wearing a conference badge. And that brings me to the other sitcom line that kept popping into my head while I was trying to explain this to myself. In The Big Bang Theory, there is a scene where a very drunk Penny says, “I think I owe you …splanation,” clearly attempting to say ‘explanation’ while her brain and mouth are no longer managed by a ‘unified control plane.’ That is exactly how MCP felt to me at first. I did not need another acronym. I needed a …splanation. A real one. Preferably in English. Preferably without requiring a PhD in distributed systems, three browser tabs of developer documentation, and someone on YouTube drawing boxes and arrows while saying “obviously” before explaining the least obvious thing I had heard all week. So this article is my attempt at that …splanation. After spending time researching MCP, I think it is important. More importantly, I think it is important in a very practical way. It is not the kind of important that requires everyone to become an AI researcher, read white papers at midnight, or pretend that “agentic workflow orchestration” is something normal people say at dinner. MCP matters because AI is moving from something that talks to something that can actually do work, and doing real work requires access to real systems. That is the part worth slowing down for. Most people first experienced modern AI as an LLM chat bot window. You typed something in, and the model responded. Sometimes the answer was impressive. Sometimes it was useful. Sometimes it was wrong with the confidence of a man giving directions in a city he has never visited. But the basic pattern was easy to understand. You asked a question. The LLM answered. That was the product experience. The problem is that most real work does not happen inside a blank chat box. Real work lives in messy places. It lives in documents, calendars, databases, code repositories, CRM systems, ticketing tools, emails, Slack messages, service logs, storage platforms, cloud consoles, spreadsheets, procurement systems, and all the other places where business reality hides after the meeting ends. That is why the first wave of AI, as magical as it felt, was also strangely trapped. A model could write a beautiful summary of a business problem, but unless you gave it the actual business context, it was still guessing. An LLM is not programmed to say “Sorry, I don’t know.” So it makes stuff up with proper grammar and punctuation. It could explain how to troubleshoot an issue, but unless it could inspect the logs, check the configuration, or look at the environment, it was still operating from theory. It could tell you how to prepare for a customer meeting, but unless it could see the account history, the open opportunities, the support cases, the renewal status, and the meeting notes from last quarter, it was basically giving you a very articulate horoscope. MCP is one of the attempts to fix that. MCP stands for Model Context Protocol. The name sounds like it was assembled by people who are very good at distributed systems and very bad at naming things for humans, but the words are actually useful. “Model” refers to the AI model. “Context” refers to the information and tools the model needs in order to be useful. “Protocol” means a standard way for systems to communicate. In plain English, MCP is a standard way for AI applications to connect to external tools and data sources. That may sound boring, but boring is often where the real technology changes happen. Nobody gets a standing ovation for plumbing until the plumbing stops working. Nobody thinks about electrical standards when they plug in a night light. Nobody wants to understand every detail of networking just to open a website. Standards become invisible when they succeed, and that invisibility is exactly why they matter. The analogy people use is that MCP is like USB-C for AI. I know that analogy is already dangerously close to becoming a bumper sticker, but it works well enough if we do not abuse it. USB-C did not make your laptop smarter. It did not make your monitor more creative. It did not make your phone more emotionally available, although at this point I would appreciate it if mine at least tried. What USB-C did was standardize connection. Instead of every device requiring its own special cable, adapter, dongle, ritual, and small sacrifice to the drawer of dead electronics, USB-C created a common interface. MCP is trying to do something similar for AI. It gives AI applications a common way to connect to the tools and data they need. The model does not need to know the internal details of every application. The application does not need to build a completely different integration for every model. MCP creates a shared language in the middle. That middle layer is what matters. Without something like MCP, the AI world runs into what technical people call the N-by-M problem. Katie Baker wrote about it last year: NxM Problem If you have ten AI applications and ten systems they need to connect to, you do not want one hundred custom integrations. If you have fifty AI applications and two hundred systems, you definitely do not want ten thousand custom integrations, unless your business model is selling painkillers to integration teams. The better model is not N times M. It is closer to N plus M. Each AI application learns how to speak the protocol. Each tool or data source exposes itself through the protocol. Once both sides understand the same standard, the number of custom connections drops dramatically. This is the point where MCP starts to become more than an AI developer convenience. It starts to look like infrastructure. To understand how it works, you do not need to become a protocol engineer. You just need to understand three roles: the host, the client, and the server. The host is the AI application the user interacts with. That could be Claude Desktop, ChatGPT, Cursor, Visual Studio Code, or an internal enterprise assistant with a name like Atlas, Navigator, Compass, or whatever else the branding team selected after eliminating “Dave.” The host is where the experience lives. It is where the user types the request, where the model reasons, and where the answer or action comes back. The client lives inside the host and manages the connection to an MCP server. You can think of it as the part of the application that knows how to speak MCP on behalf of the model. It handles the conversation between the AI application and the external capability. The server is the wrapper around a data source. There might be an MCP server for GitHub, another for Slack, another for a database, another for a filesystem, another for a CRM, another for a cloud service, and eventually one for every system that vendors decide must now be described as “AI-ready” in a press release. The server’s job is to expose what it can provide in a way the AI application can understand. It might say, in effect, “Here are the documents I can make available. Here are the actions I support. Here is the format you need to use if you want to call one of those actions. Here are the permissions required. Here is the result you can expect back.” That is where the value appears. The AI application does not need to understand every internal detail of GitHub, Slack, Salesforce, Postgres, Kubernetes, or your company’s deeply loved but spiritually exhausted internal ServiceNOW ticketing system. It needs a standard way to discover and use the capabilities exposed by those systems. MCP gives it that standard way. The protocol itself is built around a few core ideas that are easier to understand than the terminology makes them sound. MCP servers can expose tools, resources, and prompts. Tools are actions the model can ask to perform. A tool might search a database, send a Slack message, create a support ticket, run a test, update a CRM record, query an API, or retrieve the status of a system. Tools are where the AI starts moving from “I can answer your question” to “I can help complete the task.” Resources are information the model can read. These could be files, documents, schemas, database records, logs, API responses, or other pieces of context. Resources matter because AI without context is mostly a very confident intern on the first day of work. It may be talented, it may be fast, and it may be enthusiastic, but it does not know where anything is. Prompts are reusable instructions or workflows. That sounds small, but it is not. In business, consistency matters. You may not want every user inventing their own version of “analyze this account,” “review this code,” “summarize this incident,” or “prepare this forecast update.” A prompt can define how a model should approach a task, what standards it should follow, what inputs it should consider, and what kind of output is expected. Tools let the model act. Resources give the model context. Prompts help shape the model’s behavior. That combination is what makes MCP useful. Let’s make this practical. Suppose you ask an AI assistant to help prepare you for a customer meeting. Without access to your systems, the assistant can give you a generic meeting prep template. It can tell you to understand the customer’s goals, review previous discussions, identify risks, prepare discovery questions, and align to business outcomes. None of that is wrong. It is also not especially magical. It is the kind of advice that sounds helpful until you realize it could apply to almost any meeting with almost any customer in almost any industry. Now imagine that same assistant has controlled access to the right systems through MCP servers. It can read the meeting notes from prior briefings, pull the current opportunity data, review support tickets, check the renewal timeline, inspect open technical issues, summarize the customer’s stated initiatives, and identify where the account team may be telling itself a story that is more optimistic than the facts support. It can then generate a briefing that is not generic at all. It is specific, grounded, and useful. That is the difference between AI as a writing assistant and AI as a work assistant. This is why MCP keeps showing up in conversations about agents. An agent is not just a chatbot with a better title. An agent is expected to reason through a goal, choose tools, gather information, take steps, observe results, and continue until the task is complete or until it needs human help. That requires a standard way to connect reasoning to action. MCP is one of the strongest candidates for that standard layer. This is also where the MCP conversation stops being abstract for anyone running Everpure Fusion. It is one thing to say that MCP allows AI agents to connect to enterprise systems. That sounds interesting, but it can still feel like one of those technology ideas that lives safely inside a product roadmap, an architecture diagram, or a conference session where the coffee is somehow both expensive and terrible. It becomes much more practical when you look at what Everpure is doing with the Everpure Fusion MCP Server. I can almost guarantee that you will not click the link below, so I read it for you. But that will be in Part 2. I already drafted it, but I want to be respectful of your time. Not all of my readers are Everpure customers (yet). So that is my MCP “…splanation,” at least the Part 1 version. MCP is not the robot, and it is not the magical brain that suddenly makes every workflow intelligent. It is the standard connection layer that helps AI move from “I can answer your question” to “I can interact with the systems where your work actually happens.” That may not sound glamorous, but neither does plumbing, electricity, networking, or storage until something important depends on it. And that is why MCP matters. Because the next phase of AI will not be defined only by which model sounds the smartest in a chat window. It will be defined by how safely, consistently, and usefully those models can connect to real tools, real data, and real workflows. In Part 2, I will bring this closer to home and look at what this means for Everpure Fusion, because once AI starts needing context from infrastructure, the way we manage that infrastructure starts to matter a lot more. Appreciate you reading. Dmitry Gorbatov © 2025 Dmitry Gorbatov | #dmitrywashere
Hands-on with Everpure's FlashBlade//EXA
This is a syndicated repost from the WWT Company Blog. The original post can be found here. The Everpure FlashBlade and why the need for a new design The original FlashBlade was released in 2016 and was the first of its kind, delivering an all-flash solution for unstructured data, which had long been served by the spinning-disk market. With the exponential growth of unstructured data, Everpure (formerly Pure Storage) updated the FlashBlade design with a modular approach in 2022 called the FlashBlade//S that allowed compute blades to scale independently from the storage by using their DirectFlash Modules (DFMs) instead of the NAND chips being soldered onto each blade as was done in the first generation of the FlashBlade design. Despite the hardware changes, the heart of the solution (Purity//FB software) still attains phenomenal performance by using a Key-Value database as the metadata engine. In fact, the latest testing shows that a single FlashBlade//S chassis can support 3.5 trillion objects in about 100 MB of metadata space. The FlashBlade//S solution scales to 10 chassis (100 blades) and is well-suited for many AI storage use cases, such as data ingest and model training. As AI Dataset sizes increase into the petabytes, and the number of GPUs used for training and inferencing grows into the tens of thousands, the FlashBlade//S architecture doesn't scale as efficiently and economically to meet the needs; thus, the FlashBlade//EXA was born in 2025, which expanded the FlashBlade//S architecture by separating the data storage from the metadata operations. //EXA Architecture In traditional High Performance Computing and AI environments, storage systems that incorporate parallel filesystems have been dominant due to their performance, but they are also very difficult to install and complex to manage. With the maturity of parallel NFS (pNFS), we are seeing more vendors offering pNFS solutions because of the similar performance it delivers without all the extra complexity. FlashBlade//EXA utilizes pNFS in its new disaggregated storage architecture, pairing one or more FlashBlade//S500 chassis as Metadata Nodes (MN) with commodity rack servers filled with SSDs as Data Nodes (DN). This allows you to scale and size the solution based on your performance and capacity needs. How does data flow and client connections work in this new design….I'm glad you asked. When a client initiates a read or write operation, it establishes a parallel NFS (pNFS) connection to the MN. The MN acts as an "air traffic controller", redirecting the client to the appropriate DNs serving the File System for a direct access connection via the blazing-fast NFSv3 over RDMA protocol. Meanwhile, the MN(s) and DN(s) are in constant communication behind the scenes, handling file system creation and updating the metadata key-value store to keep track of where the data resides across the DNs. This architecture is purpose-built for high throughput and parallel access, ensuring that neither the metadata operations nor data access becomes a bottleneck. The results of this architecture change for FlashBlade//EXA are a high-performance, scale-out storage solution built for modern data needs. The updated design provides significant parallelism, high throughput, and the flexibility to handle both AI and HPC workloads. As Metadata requirements change, customers can simply scale the FlashBlade//S cluster from 1 to 10 chassis with each chassis supporting up to 10 blades, while still utilizing a single virtual interface port (VIP) connection that spreads the load across the cluster to utilize all the blades efficiently. As capacity needs change, simply add more DNs (up to 1000) with the SSD capacities and quantities required to meet your needs. The MNs, DNs and clients are all connected via 400 Gb network switches for low-latency, high-throughput connectivity while limiting the number of cables used to simplify the installation process. Installation Historically, Everpure's hardware appliances (FlashArray and FlashBlade) have always been just that, an appliance. Simply rack the gear, connect the cables, copy the desired software version from a USB drive, and run through the setup wizard. Within a few hours, the array would be ready to provision storage and allow client connections. In the ATC, we've installed numerous FlashArrays and FlashBlades for customer evaluations and can testify that the installation process is straightforward and quick. The FlashBlade//S (a.k.a. MN) installation was what we were used to. The recommended software version was installed on the External Fabric Modules (XFMs); we then connected the FlashBlade chassis cables to the XFMs, where the software was pushed to each of the blades and ran through the setup wizard to complete the base install steps and access it across the network. It's worth noting that any time you open up your ecosystem to use commodity servers in the design, there's going to be new challenges and growing pains around the installation, configuration, and management. And the responsibilities for securing unauthorized access and out-of-band management falls to the customer as it's no longer a hardened appliance. This was a new experience for us with Everpure as we went into this with the appliance mentality and forgetting that this design incorporated the SDS characteristics for the installation and ongoing maintenance. Note - while storage appliances typically incorporate all the firmware, drivers, and software updates as part of the upgrade process, those ongoing maintenance steps are separate tasks for the SDS approach and need to be managed by the team(s) responsible for the hardware. As it relates to management, every OEM's out-of-band management interface is different, some better than others, and requires trial and error to get it right, both on the cables/adapters used and the settings required to make a successful connection to remotely manage the device. With all that said, the rack servers (a.k.a. DNs) installation was not a simple and quick installation…but that's the beauty of the AIPG - allow WWT to iron out the kinks, prove out the steps required to make things work together, all while reducing time and risk for the entire process. The deployment in our lab sandbox consisted of a Linux management VM that runs the FlashBlade//EXA Services Container. This Services Container provides TFTP & DHCP services, a repository for installation files and scripts, and a Prometheus and Grafana instance for ongoing monitoring of the Data Node's performance. This is also were maintenance tasks, such as disk replacements, on the DNs are initiated. While this was only a small 8 DN configuration, we wanted to treat it as if it was 100, 500 or even a 1000 node install to get an idea of what a customer would expect during the installation process. While we could have simply copied the installation files and software to a USB drive to plug in locally to each server, we used the provided automation scripts and steps for the installation process by having the DNs boot over the network to load the software and configuration files from the management VM. This meant we needed to configure out-of-band networking on the DNs and change the BIOS to allow network booting. Next, we captured the MAC address for the server's onboard NICs to set up DHCP reservations and node names that would be used in the FB//EXA deployment. Finally, we configured the DHCP options to direct the DNs to the TFTP server running on the Linux VM. After a few attempts and a couple of tweaks with our management network setup, we were able to start the DN installation. The upside of troubleshooting new installations is that you really get to learn the product, how things work under the covers, and to collaborate with the OEMs so they can update their install docs and environment prerequisites to help customers avoid the same challenges in the future. In our experience, no two environments are the same; they are all configured a little differently and use different switch models and OEMs. With the base setup and deployment complete, it was time to configure the solution. At the time of our testing, the Viking VSS2320 servers are the only currently supported server model, as they provide hardware-based redundancy for high availability (HA) by allowing each server controller in the 2RU chassis to connect to all installed SSDs. In the event of a server failure, the remaining server can take over access to the drives and the data they contain. In a future software release, the resiliency will be done via software-based erasure coding, which will remove the hardware requirement for HA and allow additional server OEMs and models to be supported. Configuration FB//EXA With the Purity//DN image installed on the DNs, a few tasks remained before we could join them to the MN. For each DN, we needed to run a command to format the DN's internal storage (local NVMe drives), then another command to run a health check. Once all the DNs were in a healthy state, the last couple of steps were done via an SSH session to the MN to create the first Node Group and add the DNs to it. Note - In a large-scale FB//EXA deployment, there may be a need for multiple Node Groups (e.g., different departments or multi-tenancy), and a DN can belong to multiple Node Groups. We started with only 6 DNs in the group and later added 2 more, as shown in the image below. In the current release tested, there is no DN rebalancing of the data as reflected with DNs 9/10 having less consumed data on them. And in case you are wondering DNs 1/2 needed a firmware update at the time of the Node Group creation and will be used for future customer POCs. At this point, the system was ready to have a File System created. This step consisted of associating the File System to a single Node Group, specifying the size of the File System, and providing a name - which was all done through a single command. The only thing left to configure was the protocols enabled for the File System and the rules & policies for who can access the network share. Clients On the client side, we used two high-performant servers with GPUs and 2 x 400 Gb network cards running an Ubuntu OS. There are only a few requirements related to BGP and RoCEv2 networking that need to be configured so we installed the standard FRRouting package on the clients, enabling bgpd and configuring the service. Note - FlashBlade//EXA utilizes a common layer 3 Border Gateway Protocol (BGP) network designed for performance and efficiency, along with Remote Direct Access Memory (RDMA) that is optimized for high speed and low latency. The dual 400 Gb Connect-X network ports were then configured with the correct Priority Flow Control and DSCP mapping settings to support RoCEv2. Finally, to complete the configuration phase of the install, we installed the Everpure-provided "nfs-client-pure-dkms" Linux package, which optimizes the Linux kernel NFS. sudo apt install ./nfs-client-pure-dkms_1.0_amd64.deb Testing With the File System created on the FB//EXA and the clients configured, we were ready to start the testing. All that was left to do was mount the File System on the Clients using the below mount command that specifies the single MN VIP and File System. This is because the FlashBlade//S internally load balances the connections automatically across all the available blades. sudo mount -t nfs -o vers=4.1,proto=tcp,nconnect=16 <data_vip>:<filesystem> /mnt/nfs Note – the mount command specifies the file system type of NFS, with options for NFS version 4.1 and nconnect=16 to establish multiple TCP connections to the VIP. Here's where things got fun. During baseline synthetic testing, FlashBlade//EXA achieved near line-rate performance on a single client with dual 400 Gb ConnectX adapters. In a 100% read workload, aggregate throughput of the two 400 Gb NICs reached 781 Gb/s (97.65 GB/s), effectively saturating the available 800 Gb/s of network bandwidth on a single client. In a 100% write workload test using 512k block size a single client with two 400 Gb NICs averaged a sequential write throughput of 83 GB/s (77.3 GiB/s). As we added a second client in the mix with the same hardware specs, latency remained consistently low, and throughput scaled linearly across our tests. 100% Write across 2 x clients each with 2 x 400 Gb/s NICs In the end, we found that client-side networking was the bottleneck in our lab setup. The FB//EXA did a great job of balancing metadata operations across the blades and spreading read/write operations across the DNs that serviced the file system presented to clients. Our best guess is that it would take 8-10 clients, each with 2 x 400 Gb NICs, to saturate the network connections to the 8 DNs in our setup. Power requirements are another important factor to consider. While in an idle state, the solution consumed about ~5-6 kW of power. During the 100% write workload test using two clients, the FB//EXA solution consumed approximately 8.5 kW during sustained write tests and about 7.2 kW during sustained read tests. Summary In closing, FlashBlade//EXA is fast and made a strong impression on our AI Proving Ground team. From the disaggregated design to the simple client setup, it's a solid choice for anyone needing serious storage horsepower—especially if you want to spend more time running workloads and less time tinkering. And with FlashBlade//EXA running the same Purity//FB operating system, the learning curve will be quick for those already familiar with FlashBlade's UI. We're excited to collaborate with our customers as they explore use cases that require FB//EXA-level performance and future enhancements as the product evolves. Our initial impression is that this platform truly delivers on its promises for today's data-driven environments. Are you ready to evaluate FB//EXA for your demanding AI and HPC workloads? Let our AIPG teams help de-risk and accelerate decision-making for your next-generation, high-performance storage needs. AI Proving Ground in the ATC WWT's Advanced Technology Center (ATC) is a state-of-the-art facility that allows customers, partners, and employees to explore, test, and validate technology solutions in a collaborative environment. The AI Proving Ground (AIPG) is an initiative to develop, test, and implement artificial intelligence solutions within the ATC. The AIPG enables AI technologies to be explored, validated, and demonstrated in real-world scenarios, allowing organizations to assess the capabilities and potential of AI solutions before deploying them at scale. Technologies
