Part 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 | #dmitrywashereAsk Us Everything Recap: Rethinking Storage with the Intelligent Control Plane
The latest Ask Us Everything session focused on a topic that’s quickly becoming central to Everpure’s strategy: the intelligent control plane. And based on the questions from the community, it’s clear that many teams are starting to think beyond individual arrays and toward managing storage as a unified platform. Here are the key takeaways—driven by the questions attendees asked and the answers from Everpure experts Don Poorman, Zane Allyn and Mike Nelson. “Do I need to rebuild my automation to use Everpure Fusion?” Most teams already have automation in place, whether it’s Terraform, Ansible, or years of scripts. The good news: you don’t have to start over. Everpure Fusion is API-driven, so existing workflows can stay intact. In practice, you’re simply shifting from targeting individual arrays to targeting the fleet as a whole. That often means adding a parameter, not rewriting everything. Everpure Fusion picks up where any existing automation gets bogged down, so tasks get simpler as you scale, not more complex. The takeaway: Everpure Fusion helps you scale your existing automation—it simplifies, standardizes and extends it across your data estate. “What does API-first really mean here?” At Everpure, API-first isn’t just a label. The APIs are built before the GUI, which means everything you can do in the GUI is already available programmatically. For practitioners, that translates to flexibility. Whether you’re scripting, using infrastructure-as-code, or experimenting with AI-driven workflows, you’re not waiting for features to be exposed—you already have access. It’s a subtle difference from legacy storage, where automation often lags behind the interface. “How do I approach automation without losing control?” Attendees raised a common concern: automation can feel risky. The advice was straightforward—start with outcomes, not everything at once. Automate a single workflow, apply guardrails, and expand gradually. Automation here isn’t about removing control. It’s about: Reducing repetitive work Minimizing human error Freeing up time for higher-value tasks For most admins juggling multiple systems, that shift is practical—not theoretical. “What does this look like in real workflows?” One of the most relatable examples discussed was a ServiceNow-style request flow. Instead of manually provisioning storage across multiple systems, a user submits a request describing what they need—performance, protection, and resiliency. From there, Everpure Fusion and Pure1 handle the process automatically. The result is faster, more consistent delivery with fewer manual steps. More importantly, it abstracts the complexity away from both the admin and the requester. That’s a major difference from legacy environments, where admins must manage each step across each array. “What do I actually need to install?” This answer surprised some people. Everpure Fusion isn’t a separate product. It’s built into Purity. Once you’re on the right version (Purity//FA 6.8.1 or later, Purity//FB 4.5.5 or later), getting started is simple: Create a fleet Add arrays That’s it. No additional infrastructure, no separate control plane to deploy. This lowers the barrier significantly and makes it easy to start small and build as your needs require. “How does this scale?” As expected, scale came up quickly. Instead of managing arrays individually, Everpure Fusion introduces fleet-level management. New capabilities like topology groups allow further organization within that fleet—by region, workload, or compliance requirements. This is where Everpure’s approach really diverges from legacy storage. You’re no longer limited to thinking in terms of hardware. You can organize storage in ways that reflect how your business actually operates. “What happens if something fails?” Everpure Fusion is distributed across the arrays in the fleet. There’s no single point of failure. If one system goes offline, the rest of the fleet continues operating normally. That design keeps management resilient while still enabling centralized control. Final thoughts The biggest shift highlighted in this session is simple: Stop managing arrays. Start managing outcomes. With the intelligent control plane—powered by Everpure Fusion and Pure1—Everpure enables: Policy-driven automation Fleet-scale visibility Simpler, faster operations For storage teams, that means less time on manual tasks and more time focused on how data supports the business. And based on the conversation, that’s exactly where our customers want to go. Find out more about the Everpure Intelligent Control Plane here. Check out this and all our other Ask Us Everything sessions. And, keep the conversation going by jumping into the Everpure Community.Why Object Storage Still Matters
In Part 2, I wrote a line that, at the time, felt almost like a side comment — something I typed without fully appreciating how much it would change the direction of the story: “BREAKING NEWS: The FlashArray now supports Object??? What in the world? I may need to write an article about that!!” That reaction wasn’t planned, and it definitely wasn’t me being clever. It was me looking at the GUI and thinking, “that can’t be right… can it?” It didn’t line up with how I’ve been modeling storage architectures in my head for years, which usually means one of two things: either something fundamentally changed… or I’ve been confidently wrong about part of this for a while. And if I’m being completely honest, there was also a second reaction happening in parallel — one that I didn’t write down at the time because it sounded slightly ridiculous even in my own head: “Wait… do I actually understand why object storage exists in the first place? And more importantly… what exactly was wrong with files?” That’s the part nobody likes to admit out loud. We’ve all spent years confidently explaining block, file, and object as if we were born with that knowledge, when in reality most of us learned it incrementally, retroactively, and with just enough conviction to sound credible in front of a customer. Object storage, in particular, has always carried this aura of inevitability — like of course it’s better, of course it scales, of course it’s what modern applications need — without always forcing us to question why the previous model stopped being enough. Because for as long as most of us have been designing infrastructure, object storage has not simply been another protocol layered onto an existing system. It has represented a fundamentally different way of organizing and accessing data, one that required its own architectural approach, its own scaling model, and, more often than not, its own dedicated platform. The separation between block, file, and object was not arbitrary; it was a reflection of how deeply different those paradigms were in terms of metadata handling, access patterns, and performance expectations. This is precisely why platforms such as Everpure FlashBlade exist in the first place. They were not created as extensions of traditional storage systems but as purpose-built architectures designed to treat unstructured data — and particularly object data — as a first-class citizen. The use of distributed metadata services, sharded across independent nodes, combined with a key-value store storage model, allows such systems to achieve levels of parallelism and throughput that simply cannot be replicated within a controller-based design. In that context, object storage is not something that is “added” to the system; it is the system. Which is why seeing S3 support appear on FlashArray required a pause. Not excitement. Not skepticism alone. Something closer to intellectual friction. Reconciling Two Architectural Worlds The most important step in understanding what FlashArray has introduced is to resist the temptation to treat it as a direct comparison to FlashBlade. These aren’t two different ways of solving the same problem. They’re two different answers to two different problems—and pretending otherwise is where people get themselves into trouble. FlashBlade is built for object, not adapted to it. S3 talks directly to a distributed engine that thinks in objects, not files pretending to be objects. Metadata is spread across blades instead of becoming a centralized choke point, and the whole system scales the way modern workloads actually need it to. There’s no file system layer to fight with, no directory structure to navigate, no POSIX semantics getting in the way. It just does what you’d expect when you remove all of that: it goes fast, it scales cleanly, and it keeps up with workloads like HPC, AI and analytics without breaking a sweat. FlashArray takes a very different path, and in reality, it’s not what most people expect. It doesn’t try to reinvent itself as an object platform, and it doesn’t throw an S3 gateway in front of the array and call it a day. With Purity 6.10.5+, S3 just shows up as another protocol the system understands, right next to block and file. That distinction matters more than it seems. This isn’t something duct-taped on the side — it’s part of the same control plane, the same data path, the same system you’ve already been running. But let’s not pretend it turned into FlashBlade overnight. This is still a controller-driven architecture. The primary controller does the heavy lifting — handling requests, authenticating them, coordinating operations — before anything actually hits the storage engine. Which means it behaves differently, especially as workloads scale. So it ends up in this interesting middle ground. Not a native object system in the pure sense, but not a hack either. Just a different way of exposing what’s already there. The Translation Layer and Its Consequences It would be irresponsible to discuss FlashArray S3 without explicitly addressing the implications of this design. Even with its native integration into Purity, S3 operations are still subject to the realities of a controller-bound architecture. Every request must be processed, authenticated, and coordinated before it is executed, introducing a measurable difference in behavior compared to both native block operations and distributed object systems. The most immediate effect is latency. While FlashArray continues to deliver sub-150 microsecond performance for block workloads, S3 operations typically operate at higher latencies (in 1 millisecond range) due to the additional processing steps involved. This is not a flaw; it is the natural outcome of introducing a protocol that was designed for scale and flexibility into a system optimized for low-latency transactional workloads. Metadata handling further reinforces this distinction. FlashBlade distributes metadata across its architecture, enabling massive parallelism and consistent performance at scale. FlashArray processes metadata through its controller framework, which introduces natural serialization points under high concurrency. As workloads become increasingly metadata-heavy — particularly with small objects — this difference becomes more pronounced. The system also enforces clearly defined operational limits to maintain predictable performance. As of Purity 6.10.5+, FlashArray supports up to 250 S3 buckets per array and a maximum of 1,000,000 objects per bucket. FlashArray Object Store Limits Object storage operates at the array scope and does not integrate with multi-tenancy or “realms”, which has implications for service provider models and strict tenant isolation requirements. These constraints are not arbitrary limitations; they are guardrails that ensure the system behaves consistently within its architectural boundaries. Where the Architecture Becomes Secondary Having established those boundaries, the conversation naturally shifts from “how it works” to “why it matters”. In many enterprise environments, particularly within SLED organizations, the challenge is not achieving exabyte-scale throughput or supporting billions of objects. The challenge is delivering capabilities in a way that is operationally sustainable, economically efficient, and aligned with existing infrastructure. This is where FlashArray’s approach becomes compelling. By exposing object storage within the same platform that already supports block and file workloads, it eliminates the need to introduce a separate system, a separate operational model, and a separate set of dependencies. The same management interface, the same automation framework, and the same data services extend across all protocols. More importantly, object data inherits the full set of Purity capabilities. Global inline deduplication and compression apply to S3 workloads, significantly improving storage efficiency compared to many object-native platforms. SafeMode snapshots extend immutability to object storage, providing a critical layer of protection against ransomware. ActiveCluster, combined with ActiveDR, enables a three-site resilience model that ensures data availability across multiple locations with zero RPO between primary sites. These are not incremental improvements. They represent a shift in how object storage can be consumed within an enterprise. Practical Use Cases in a Unified Model When viewed through this lens, the use cases for FlashArray S3 become both clear and grounded in reality. Development and Staging Environments Some applications rely on S3 APIs but do not require massive scale, FlashArray provides a consistent and integrated object interface without introducing additional infrastructure. Developers can build and test against a familiar model while remaining within the same operational environment. Backup and Recovery Workflows FlashArray S3 enables modern data protection strategies that leverage object storage while benefiting from flash performance, deduplication, and indelible snapshots. This combination improves both recovery times and storage efficiency. Tier-two repositories and application-integrated storage represent another natural fit. Workloads such as document management systems, logs, and archival data often require object semantics but do not justify the higher cost of a dedicated object platform. Consolidating these workloads onto FlashArray simplifies operations while maintaining reliability and performance. Where the Boundaries Still Matter None of this diminishes the importance of selecting the appropriate platform for workloads that demand a different architecture. High-performance AI pipelines, large-scale analytics environments, and use cases requiring massive parallelism remain firmly within the domain of FlashBlade. The ability to scale performance linearly, distribute metadata across many nodes, and support billions of objects is not optional in these scenarios — it is essential. What has changed is not the relevance of those systems, but the necessity of deploying them for every object storage use case. A Subtle but Significant Shift The introduction of S3 on FlashArray does not represent a replacement of one architecture with another. It represents a convergence of capabilities within a unified operational framework. Object storage, in this model, is no longer a destination that requires its own platform. It becomes a capability — one of several ways to access and manage data within the same system. That shift is easy to overlook, but its implications are significant. It allows organizations to design around outcomes rather than protocols, to reduce complexity without sacrificing capability, and to align infrastructure more closely with the needs of modern applications. Closing Reflection Looking back at that line in Part 2, it is clear that the reaction was not just about a new feature appearing in the interface. It was about the recognition — however incomplete at the time — that something foundational was beginning to change. Object storage did not suddenly become simpler, nor did it lose the architectural complexity that defines it. What changed is where it lives. And once that becomes clear, you start asking a slightly uncomfortable but very honest question: If this works… and it works well enough for most of what I actually need… why was I so convinced it had to live somewhere else in the first place? That is usually where the interesting work begins. Appreciate you reading. Dmitry Gorbatov © 2025 Dmitry Gorbatov | #dmitrywashereFusion for the Win: You No Longer Have to Decide Where the Data Lives
Dmitry Gorbatov Apr 10, 2026 In the first post, I walked through enabling file services on a FlashArray. There was nothing particularly complicated about it. The process was clean, predictable, and by the end of it I had a fully functional file platform running on the same system that was already supporting the rest of the environment. It behaved exactly the way you would expect it to behave. And that is precisely what started to bother me. Because if you step back and look at what we actually did, the workflow has not really changed in years. I still made a series of decisions in a very specific order. I chose where the workload should live, I created the file system, I attached protection, and I made sure everything was named and organized in a way that made sense at that moment. It was structured. It was controlled. It was also entirely dependent on me. That model works well enough when the environment is small or when the same person is making the same decisions repeatedly. But as soon as you introduce scale, or simply more people, those decisions start to drift. Not in a dramatic way, but in small inconsistencies that accumulate over time. A slightly different naming convention here, a missed policy there, a workload placed somewhere because it “felt right.” Nothing breaks. It just becomes harder to operate. When the model stops making sense What stood out to me after going through the manual process is that we are still treating storage as something that needs to be individually managed, even though the platform itself has already moved beyond that. We have systems that can deliver consistent performance, global data services, and non-disruptive operations, yet we still rely on human judgment to decide where things go and how they should be configured. That disconnect is where Everpure Fusion begins to make sense. Not as an additional feature, but as a way to remove an entire class of decisions that we have simply accepted as part of the job. From managing infrastructure to defining intent The idea behind the Enterprise Data Cloud is not particularly complicated, but it does require a shift in perspective. Instead of treating each array as a separate system with its own boundaries, the environment becomes a unified pool of resources. Data is no longer something that you place on a specific array. It is something that exists within a global pool, governed by policies that define how it should behave. Once you start thinking this way, the questions change. You are no longer asking where a workload should go. You are asking what that workload needs to look like. Performance expectations, protection requirements, naming, and lifecycle behavior become the inputs, and the system automation takes responsibility for everything else. That is the role of Everpure Fusion. What actually changes in practice The easiest way to understand Fusion is to look at what it removes. In the manual model, every step is explicit. You build storage object by object, and then you attach policies to those objects. You rely on memory, experience, and sometimes documentation to make sure everything is done correctly. With Fusion, that entire process becomes declarative. Instead of building storage step by step, you define a preset. A preset is a reusable definition of what “correct” looks like for a given workload. It captures performance expectations, protection policies, naming conventions, and any constraints that should apply. Once that definition exists, it becomes the standard. When you create a workload from that preset, Fusion evaluates the environment and places it on the array that best satisfies those requirements. It creates the necessary objects, applies the policies, and ensures that everything is consistent with the definition. The important shift is not that tasks are automated. It is that decisions are no longer made ad hoc. Trying it in the lab After building file services manually in the previous post, I wanted to see what this would look like using the same environment, but driven through Fusion. I started by defining a fleet, grouping the array into a logical boundary where resources and policies could be managed collectively. Once the array becomes part of a fleet, you stop thinking of it as an individual system and start treating it as part of a shared pool. From there, identity becomes the next requirement. Fusion relies on centralized authentication, typically through secure LDAP backed by Active Directory. This is what governs access to presets and workloads, and it ensures that everything aligns with existing organizational controls. Up to this point, everything felt exactly like I expected. Then I moved to the part I was actually interested in. Where things didn’t quite line up The goal was to take the file services I had already built and express them as a preset. I wanted a single definition that would describe the file system, its structure, its policies, and its behavior, and then use that definition to create workloads without going through the manual steps again. Conceptually, that is exactly what Fusion is supposed to do. In practice, I ran into a limit that I had not fully appreciated at the start. I was running Purity OS 6.9.2. Which, to be fair, is where most production environments should be. It is a Long-Life Release, stable, predictable, and already capable of delivering Fusion for fleet management, intelligent placement, and policy-driven storage classes. You can create Presets and Workloads for block workloads. What it does not include is full support for File Presets on FlashArray. That capability, where a file system, its directories, and its access policies are all defined and deployed as a single unit, arrives in the 6.10.X Feature Release line. Which means that the exact outcome I was trying to demonstrate was sitting just one version ahead of me. This is where I had to laugh at myself There is always a moment in a lab where you realize that the limitation is not the platform. It is you. In this case, it was me getting ahead of the version I was actually running. My intentions were “ever” so “pure” (IYKYK). The execution was slightly behind the feature set. So I upgraded One of the advantages of working with this platform is that upgrading does not carry the same weight it used to. The system is designed for non-disruptive operations, and moving between versions does not require downtime or migration. The upgrade to 6.10.5 was uneventful in the best possible way. Controllers were updated in sequence, workloads continued to run, and the system transitioned to a new set of capabilities without introducing risk. There is something very satisfying about performing an upgrade not because something is broken, but because you want access to what comes next. BREAKING NEWS: The FlashArray now supports Object??? What in the world? I may need to write an article about that!! When it finally clicks Once on 6.10.5, the model finally aligns with the intent. Once I clicked on Create Your First Preset, it gave me these options: I defined a preset that described the file workload I had previously built manually. It included the expected behavior, protection policies, and naming conventions. Instead of creating individual components, I was defining the service as a whole. Now this was really neat - when you select Storage Class, it knows that arrays are available in your environment. In my case, I only have FA //X. At this point a new field opens and allows you to select the Storage Resources. Once I hit “Publish'“ this was the result: Think of this entire process like this: Define your Recipe (Preset) Order from the Menu (Workload) Lets create a workload from that preset. Once I clicked on + to add a new Workload, the Wizard opened: Give a name to that Workload: Since Fusion Fleet has both of my lab arrays, I have an option to select an array for the workload placement. Our of curiosity I clicked: “Get Recommendations” and this was the result: Once I hit Deploy, within seconds, the workflow executed and I had my File System created. How awesome is this? Come on, give me a cheer! Think about the magnitude of what just happened. I provided minimal input, and Fusion handled the rest. It selected the appropriate array based on capacity and performance, created the file system, applied the policies, and ensured that everything matched the definition. There was no second pass. There were no additional steps. The outcome matched the intent. By moving to this model, I just shifted from being a "storage admin" to a "data architect." I defined the outcomes and it happened “automagically”. Why this matters more than efficiency It would be easy to describe this as a way to reduce manual effort, but that misses the point. The real value is consistency. When every workload is created from a defined preset, variability disappears. Policies are enforced by default. Naming is consistent. Placement is based on a complete view of the environment rather than individual judgment. Over time, that consistency reduces operational friction and lowers risk in ways that are difficult to measure but easy to recognize. Environments behave predictably, scaling becomes simpler, and the likelihood of human error decreases. Where this leads In the first post, I showed that file services can run natively on the array without additional infrastructure. In this post, the focus shifted to removing the manual decisions involved in building and managing those services. The next step is where things move beyond automation. As capabilities like ActiveCluster for File continue to evolve, the conversation shifts toward mobility and continuous availability. At that point, it is no longer just about simplifying operations, but about removing the constraints that tie workloads to a specific system or location. That is a conversation for Part 4. Appreciate you reading. © 2025 Dmitry Gorbatov | #dmitrywashereStop Running File Servers on VMs
Dmitry Gorbatov Apr 06, 2026 One of the superstar Pre-Sales Systems Engineers on my team was in a customer meeting not too long ago, walking through what was, by all accounts, a well-run environment. The team knew what they were doing, the infrastructure was stable, and nothing stood out as particularly problematic. It was one of those conversations where everything feels “fine,” which in our world usually means there are inefficiencies hiding in plain sight. Then he started asking questions about enterprise file services. They were running a couple of Windows Server virtual machines on top of VMware vSphere, serving SMB shares to the rest of the organization. Again, nothing unusual there. This is still the default design in a lot of places, and it works well enough that nobody feels compelled to question it. But as the meeting on, a few details started to surface. One of the VMs was consistently running hot during backup windows. Another one hadn’t been patched in a while because nobody wanted to risk disrupting access to shared data. The storage policies applied at the VM layer didn’t quite line up with what was actually configured on the array. And there was an unspoken understanding that maintaining these systems was just part of the job — something you deal with, not something you optimize. What made it more interesting was that the same environment had an Everpure FlashArray running their critical workloads. It was handling databases, transactional systems, and anything else that required consistent performance and reliable data services. It was protected, replicated, and trusted. File services, however, were living on top of virtual machines, with their own lifecycle (please, please… don’t say VMware snapshots), their own dependencies, and their own set of operational overhead. That disconnect is what stuck with me. So instead of continuing the theoretical discussion about architecture and “best practices,” I went back to my lab and decided to try something very simple. I wanted to see what would actually happen if I enabled file services directly on the array and treated it as a first-class file platform instead of assuming that role belonged to something else. There was no redesign exercise, no migration plan, and no phased rollout. I wasn’t trying to prove a point on a whiteboard. I just wanted to turn it on and see if the experience matched what we tend to claim in conversations. Nothing broke. Nothing felt forced. And more importantly, nothing about it felt like a compromise. This post walks through exactly what I did to enable and run file services on a FlashArray //X20R4 running Purity 6.9.2. The goal is not to explain the architecture in abstract terms, but to show how straightforward it is to take something that already exists in your environment and use it in a way that removes unnecessary complexity. What I realized (and why this matters) Once everything was up and running, the first realization was that this is not a workaround or a secondary feature designed to fill a gap. FlashArray File is integrated into the platform in a way that makes it behave like a natural extension of what the system already does well. It uses the same controllers, the same global storage pool, and the same data services that are already in place for block workloads. There is no separate management layer, no additional appliance (remember Data Movers and NAS Personas?), and no need to think about it as something different from the rest of the system. That by itself is useful, but it is not the most important part. What stood out more was the amount of operational overhead that simply disappeared. When file services run on virtual machines, you inherit everything that comes with them. You are responsible for the guest operating system, including patching cycles, security updates, and the occasional issue that appears at the worst possible time. You are also consuming hypervisor resources and, in many cases, paying for licensing that exists solely to support a function that could be handled elsewhere. On top of that, you end up managing data protection, performance, and capacity in two different places (remember RDMs, or in-guest iSCSI?), which introduces opportunities for inconsistency. By moving file services onto the array, that entire layer is removed. You are not just changing where the workload runs; you are simplifying how it is operated, protected, and maintained over time. The second realization was that this approach aligns with where things are clearly heading. Everpure is already extending these capabilities with ActiveCluster for File, which will bring synchronous replication and continuous availability to unstructured data. I do not have that running in my lab yet, but it is not difficult to see the direction. As those capabilities become more widely available, the remaining reasons to maintain separate file platforms will continue to shrink. That will be a conversation for a future post. Let’s tentatively call it Part 3 of the series. Before you start (the part that actually matters) Enabling file services on the array is straightforward. The part that tends to create friction is everything that surrounds the configuration, particularly networking and integration with existing services. The first consideration is the choice of network interfaces. Although the array provides 1GbE management ports, those interfaces are not intended for serving file workloads. Using them for SMB or NFS traffic introduces an artificial bottleneck that will affect performance and, more importantly, perception. File services should be configured on the 10 or 25GbE data ports, which are designed to handle production traffic and provide the throughput expected from the platform. Here is what my array looked like earlier today: The highlighted ports are ETH10 and ETH11 on both controllers. Redundancy should be planned, but it does not need to be over engineered. A simple and reliable starting point is to use at least two ports per controller, ensuring that the configuration remains consistent across both sides. The goal is to achieve predictable failover behavior rather than to build a complex network design that is difficult to troubleshoot. One concept that is worth understanding early is the File Virtual Network Interface, or File VIF. This is the logical identity of the file service—the IP address that clients use to connect. It is designed to move between controllers as needed, maintaining availability during failover events. Once this concept is clear, the rest of the networking configuration becomes much easier to follow. My lab was built within budgetary constraints - that means I don’t have separate ethernet switches and I don’t have the time to build a separate DNS Server for FA File Services. Everpure recommends separating file client traffic from management traffic, but that’s a best practice, not a requirement. Since my lab switch is a single flat, untagged network and the environment is really just 192.168.1.0/24, I will just us the most practical approach - put the FA File VIFs on that same 192.168.1.0/24 network with their own IP addresses. Here is what I did: I just kept the file VIFs on 192.168.1.0/24 since that is the only real network available. FlashArray expects unique layer-3 subnets and does not support overlapping networks. DNS In my specific configuration, I don’t need a new DNS server. My existing management DNS servers can resolve the AD/DC hostnames and the FA File names/computer object. FA File can use the same DNS as management with no extra file-DNS configuration. By default, DNS lookups will go out the management interfaces, so my DNS server just needs to be reachable from the management network. And it is. Let’s turn the lights on, shall we? After assigning the IP addresses and enabling the ports, the lights came on. Important design note I will use one client-facing VIF IP for the file service, for example: File VIF IP: 192.168.1.135 Netmask: 255.255.255.0 Gateway: 192.168.1.2 default gateway Do not try to use 192.168.1.131-134 as four separate FA File IPs unless you intentionally want multiple VIFs. The ct*.eth* ports are transport underlay, not the SMB/NFS endpoint IPs. Configuring a File Server and File VIF Open the File Services server page Go to Storage → Servers. Open the default server (_array_server) or create a new file server if you want a dedicated namespace. Stay on that server’s details page. 3. Create the File VIF Use physical bonding first; it’s the simplest. In the Virtual Interfaces section, click + Create VIF. Choose Physical Bonding. Select the underlying port pairs: Pair 1: ct0.eth10 and ct1.eth10 Pair 2: ct0.eth11 and ct1.eth11 Name the VIF something simple, e.g.: filevip1 Enter network settings: IP Address: 192.168.1.135 Netmask: 255.255.255.0 Gateway: 192.168.1.1 Leave VLAN blank since there are no VLANs. Save and Enable the VIF. That creates the client-facing IP for SMB/NFS. 4. Configure DNS Integration with DNS and Active Directory is another area where a bit of preparation goes a long way. File services rely on proper name resolution and domain integration, and it is important to recognize that file-related DNS settings are separate from the array’s management DNS configuration. The system effectively becomes a participant in the domain as a file server, which means that DNS records, domain join operations, and permissions should be planned accordingly rather than improvised during setup. Since my DNS is 192.168.1.2 and I want to reuse management DNS: Go to the server’s DNS Settings. My management DNS is already configured and points to 192.168.1.2 If you want to explicitly add file DNS: Click + in DNS Name: file-dns Domain suffix: your AD/domain suffix DNS server: 192.168.1.2 Service: file Source interface can remain default unless you specifically need file VIF sourcing. 5. Create required DNS A records On my DNS server 192.168.1.2, I created an A record for the file service name pointing to the File VIF IP. Name: fa-file01 IP: 192.168.1.135 If you are joining AD for SMB/Kerberos: Make sure DNS also has A records for all relevant domain controllers. Create the A record that matches the AD computer object / FA File service name. 6. Join Active Directory or configure LDAP If using SMB Use Active Directory. Go to: Storage → Servers → _array_server Then look for one of these panels: Remote Directory Service Click Edit Configuration Select Active Directory Enter: Name Domain DNS Name Computer Name Use Existing Account if applicable AD User Password TLS Mode Save / Join This part took me 2 hours. I was getting some crazy error messaged that I’m simply embarrassed to share here. It was not the DNS. It was an NTP server misconfiguration that was causing Kerberos to not authenticate properly. There was a 10 minute time skew between the FlashArray and the domain controller. 7. Create a File System The file system is the top-level container for your unstructured data. GUI Method: Navigate to Storage > File Systems and click the plus sign (+). Enter a name and click Create. CLI Method: Use the following command: purefs create <file-system-name>. 8. Create a Managed Directory Managed directories allow you to apply specific policies (like quotas or snapshots) to subfolders within a file system. GUI Method: Go to Storage > File Systems. Click on the name of the file system you just created. Select the Directories tab and click the plus sign (+). Enter the directory name and the internal path (e.g., /users). CLI Method: Use the following command: puredir create filesystem1:users --path /users. 9. Create an Export The export makes the managed directory accessible to clients over the network. GUI Method: Navigate to Storage > Policies > Export Policies. Select an existing policy (e.g., a standard SMB or NFS policy) or create a new one. Within the policy view, click the plus sign (+) to add an export. Select your Managed Directory, choose the appropriate Server (use _array_server for standard configurations), and provide an Export Name (this is the name clients will use to mount the share). CLI Method: Use the following command: puredir export create --dir <file-system-name>:<directory-name> --policy <policy-name> --server <server-name> --export-name <client-facing-name>. A quick validation step At this point, it is worth validating access from a client system. Map the SMB share and perform a simple set of operations—create files, read data, and verify permissions. This is less about testing performance and more about confirming that networking, authentication, and access controls are behaving as expected. In most cases, if the earlier steps around DNS and Active Directory were done correctly, this validation step is uneventful, which is exactly what you want. And now let the data migration begin. I am actually doing it from my Mac. And it just works!!! What becomes apparent after completing these steps is how little effort is required to stand up a fully functional file platform on infrastructure that is already in place. Unless, of course, your NTP server crashed. The system behaves predictably, integrates cleanly with existing services, and avoids many of the operational burdens associated with VM-based file servers. And that is where things start to get interesting. Because everything described so far is still being done manually—selecting where things live, defining configurations, and applying policies one step at a time. It works, and it works well, but it also mirrors the way storage has traditionally been managed. In the next post, I will show what happens when you stop doing these steps manually and let Pure Fusion handle placement, policy, and provisioning instead. Appreciate you reading. © 2025 Dmitry Gorbatov | #dmitrywashere
What We Learned About ActiveCluster for File from the Latest “Ask Us Everything”
The newly-announced ActiveCluster for file extends Everpure’s synchronous replication to unstructured workloads–so it was no surprise that the latest Ask Us Everything session drew a lot of attention. Attendees came ready with practical questions about how it works, where it fits, and what it could mean for real production environments. And host Don Poorman, Product Manager Quinn Summers, and Principal Technologist Russell Pope brought the Everpure answers. The conversation showed just how this new approach can help modernize resiliency, mobility, and day-to-day operations. Let’s break down the biggest takeaways. “Is This Just HA… or Something More?” One of the most interesting threads came early: is ActiveCluster for file just another high availability solution? Short answer: no. Attendees pushed on this, and the response from Everpure’s team was clear—this is about data mobility and policy-driven management, not just surviving a failure. Instead of treating HA as a one-off configuration, ActiveCluster is designed to align storage behavior with business intent. That shift matters. In traditional environments, HA is often bolted on and managed manually. Here, policies define things like performance, protection, and placement—and the system enforces them automatically across the fleet. For many in the session, that was a “wait, this is different” moment. The Big Comparison: Legacy Replication vs. ActiveCluster A standout question came from someone evaluating ActiveCluster as a replacement for legacy approaches like NetApp SVMDR. The discussion highlighted a key difference: granularity and consistency. Legacy solutions often replicate at a coarser level (think entire systems or large aggregates), which doesn’t always align with how applications are structured. ActiveCluster instead works at the realm level, where both data and configuration are synchronously mirrored. That means: No mismatched failover scope No rebuilding configs on the other side No “did we forget something?” during a failover It’s a cleaner, more application-aligned model—and that resonated with the audience. “What Actually Happens During a Failover?” Attendees asked the right questions: Is failover automatic? What about DNS changes? How fast does it happen? The answers were refreshingly direct. In a stretched Layer 2 setup, failover is fully automatic and transparent—clients don’t even notice. In more complex network designs, there may be some redirection (like DNS updates), but the data is already in sync. And timing? The expectation is on the order of seconds (often under 10). This is a variable currently unmatched by any legacy storage competitor to Everpure. There was also a lot of interest in how Everpure avoids split-brain scenarios. The mediator service—hosted by Everpure or deployed locally if needed—acts as a lightweight “tie breaker” during network partitions. No extra infrastructure to manage in most cases, and no guesswork about which side should stay active. Simplicity Came Up… A Lot If there was one theme that kept coming back, it was simplicity. One attendee asked about setup, and the answer was basically: it’s wizard-driven. That sparked a broader discussion about how legacy storage often assumes admins have time to relearn complex workflows. In reality, most teams are juggling multiple systems. The ability to stand up synchronous replication with a few guided steps—not scripts, not custom tooling—landed well. Even testing reflects that philosophy. Instead of complex test procedures, the guidance was simple: pull cables, simulate real failures, and observe behavior. No artificial “test modes”—just real-world validation. Data Mobility Is the Real Story Another strong theme was mobility. ActiveCluster doesn’t just protect data—it enables you to move it. The “stretch and unstretch” workflow means datasets can be mirrored, shifted, and re-homed without disruption. That’s a big departure from traditional models, where moving data often means downtime, migration projects, or both. For teams thinking about workload placement, lifecycle management, or hybrid environments, this opens up new options. Real-World Use Cases The audience also pushed beyond file shares into real workloads: Financial trading and payment systems Healthcare imaging and research data VMware/NFS environments The takeaway: if it’s mission-critical and file-based, it’s a candidate. Final Thought: Even More on the Horizon Even with some initial constraints (like starting with new file systems), the field feedback shared during the session was telling: customers are ready to adopt this early. Why? Because the core value—resiliency, mobility, and simplicity—is already there. And if the session proved anything, it’s that Everpure is building this in close collaboration with the community. The questions weren’t just answered—they’re shaping what comes next. If you’re evaluating how to modernize file services, Everpure’s approach is definitely one to consider. Check out this and all our other Ask Us Everything sessions. And, keep the conversation going by jumping into the Everpure Community.The Great Rebalancing: The Software Selloff is Supercharging Data Infrastructure
The Great Rebalancing: Why the Two-Trillion-Dollar Software Selloff Is the Best Thing That Ever Happened to Data Infrastructure Two trillion dollars has been wiped from software stocks in 2026, the largest AI-driven selloff in history. But unlike the four prior software crashes (2000, 2008, 2016, 2022), this one isn't caused by speculation, macro, or rates. For the first time, AI can actually do what the software does. Gartner says 35% of point-product SaaS gets replaced by 2030. But the headlines miss the real story. Enterprise software doesn't die. The interface dies. Every decade for 30 years, the way humans interact with systems has changed: green screens to client-server to web to SaaS to AI agents. The data persists through every transition. The infrastructure underneath is the only truly durable investment. Three forces are converging: AI acceleration ($37B in enterprise GenAI spending), software deflation (seat-based pricing collapsing), and threat escalation (Anthropic just withheld their Mythos model because it autonomously found vulnerabilities in every major OS, bugs missed for 27 years). Meanwhile, NAND flash is in a global shortage, making every storage platform decision strategic. The thesis: the interface is temporary, the data is permanent, and the infrastructure that makes data accessible to whatever comes next is the competitive weapon. That's why Everpure built the Enterprise Data Cloud: six requirements (unified data, autonomous governance, built-in cyber resilience, Evergreen architecture, dataset intelligence, delivered as a service) in the only architecture that delivers all six.
Ask us Everything About ActiveCluster for File
April 3 | Register Now! Got questions about continuous file access and true mobility? Get answers. This month we’re diving into ActiveCluster™ for File and how the Everpure Platform delivers synchronous file replication as a native capability. As file data grows and availability requirements increase, infrastructure teams need data mobility and business continuity that’s automated, transparent, and built into the platform they already trust. You’ll get an overview of how ActiveCluster for File enables: Continuous file access during migrations, maintenance, and failover Simple setup driven by Everpure Fusion™ with policy-based automation Granular, workload-level failover control Zero RPO and RTO availability without added hardware or complexity Bring your questions for our experts, who are ready to discuss file mobility use cases, business continuity best practices, and how to extend the value of your Everpure platform with built-in, next-generation file resilience. Register Now!Ask us Everything About Storage for Databases
March 19 | Register Now! Got questions about running databases at scale? Get answers. This month we’re diving into databases—and how Everpure (formerly Pure Storage) helps infrastructure teams support modern, mission-critical workloads with less risk and operational overhead. You’ll get an overview of how our next-generation storage platform enables: Consistent performance and scale for AI-enabled database workloads Simpler, automated operations with Everpure Fusion™ presets and workload setups Built-in cyber resilience and high availability Predictable costs with non-disruptive upgrades Bring your questions for our experts, who are ready to discuss database use cases, deployment considerations, and how to modernize database infrastructure with confidence. Register Now!
