Get rid of stressful infrastructure headaches. Everpure Fusion handles your data, autonomously.
It happens! It's 11:45 PM on a Friday. An alert fires and storage latency has spiked across a production workload. The operator digs in and traces it back to an automated tiering policy that quietly moved a hot dataset to a slower tier because it looked idle based on a 24-hour access window, right before a scheduled batch job that runs every weekend. Nobody changed anything. The policy did exactly what it was configured to do. But nobody remembers configuring it that way, the documentation hasn't been touched in two years, and the monitoring dashboard shows storage as "healthy" because utilization is fine. It's just in the wrong place. The operator overrides the tier, performance recovers, and spends the next hour writing an incident report for a problem that shouldn't exist. A system that was supposed to make life easier made a decision with no context, no warning, and no visibility into why. That's the anger that doesn't go away quickly. It's not just frustration at the incident. It's the feeling that the tools are working against you instead of with you. The core problem in that story was a system making decisions with no context, no warning, and no visibility. Everpure Fusion attacks each of those problems: Unified visibility across the entire fleet: Everpure Fusion provides a global dataset as a single source of truth for discovery, management, and configuration of storage arrays so the operator isn't piecing together what happened across multiple dashboards, multiple arrays, multiple tickets after the fact. They see the full picture in one place, before things go wrong. Intelligent workload placement: Rather than static policies quietly acting on stale access patterns, Everpure Fusion uses AI-guided placement to boost performance and efficiency for every workload. It understands workload behavior, not just utilization snapshots, the kind of context that would have caught a batch job pattern before tiering the dataset down. Policy-driven governance with real control: Automated orchestration cuts manual tasks and speeds service delivery, while unified controls simplify audits, reduce risk, and prove compliance fast. Policies are visible, documented, and governable. Not buried configs nobody remembers setting. Built into the platform, not bolted on: Evepure Fusion is now simply a part of Purity, meaning it is not an add-on you have to install or buy, but rather a core piece of the Purity operating system. The operator doesn't have to manage another tool. The intelligence is already there. The operator in that story didn't need more alerts. They needed a system that understood context, made decisions transparently, and gave them control without requiring them to be online at midnight to maintain it. That's exactly the gap Everpure Fusion is designed to close - with One Fleet, Zero Complexity. Why policy-driven storage operations matter Everpure Fusion is built as the core of Everpure intelligent control plane that manages all arrays including FlashArray, FlashBlade, and cloud as a unified fleet, with one topology, one API, and one operational framework regardless of protocol or local - datacenter, cloud or edge. That uniformity is what makes policy enforcement reliable at scale. Everpure Fusion introduces workload-based provisioning through presets, which are predefined policy-driven templates for specific workload types, encoding protection policies, replication, and SafeMode retention from the moment a workload is provisioned, not patched in after an incident. Admins no longer need to pre-plan and tune deployments manually, which reduces the risk of non-compliance and improves resiliency by ensuring workloads are provisioned correctly from the beginning. The result is infrastructure that enforces your intent, not just your last manual action. Intelligent placement, rebalancing, and fleet-scale capacity control If you manage storage at scale, you've probably seen this scenario play out more than once. One array is buried, running hot, and screaming for relief. Three aisles over, another array is sitting at 40% utilization, doing almost nothing. And somewhere in between, your team is scrambling to provision capacity, kick off an emergency migration, and explain to stakeholders why an SLA was missed on a workload that, in hindsight, never should have been placed there in the first place. This is not a people problem. It is a tooling problem. And it is remarkably common. Everpure Fusion starts solving this problem at the moment of provisioning. When a new workload lands, most storage systems do a simple capacity check and place it wherever space is available. Everpure Fusion does something fundamentally different. The placement engine evaluates every array in the fleet simultaneously, looking at IOPS headroom, throughput capacity, and physical utilization before making a decision. The goal is not just to find somewhere to put the workload. It is to find the right home for it, one where it can live comfortably for the long term without creating a bottleneck down the road. Think of it as placing workloads with intention rather than convenience. Of course, environments do not stay static. Workloads grow, usage patterns shift, and an array that looked healthy six months ago can become a problem today. Everpure Fusion accounts for this with continuous rebalancing built directly into its operation. When an array starts trending toward overload, Everpure Fusion detects it and begins orchestrating data movement across the fleet automatically. No manual intervention required. No application downtime. Data migrates in the background while workloads keep running, and arrays that were sitting underutilized suddenly become productive members of your infrastructure. At fleet scale, now supporting up to 64 arrays, this turns capacity management from a constant firefight into something that largely runs itself. What makes this possible without disruption is how Everpure Fusion executes the move under the hood. It leverages ActiveCluster to stretch the volume across both the source and target arrays simultaneously, creating a synchronous mirror in place. Once the stretch is established, volumes are connected on the target array and hosts auto-discover the new target paths through standard multipathing. The target then validates that path usage is healthy and confirmed before any cutover begins. Only after that validation is complete are the volumes disconnected from the source, ensuring there is zero gap in access at any point in the sequence. Everpure Fusion then unstretches from the source array to complete the rebalance and release its capacity. The result is a seamless, non-disruptive migration that the application never sees. What truly sets Everpure Fusion apart from a standard load balancer is what happens under the hood. Powered by Pure1 AI and up to 30 days of historical workload data, Everpure Fusion does not just look at what is happening right now. It looks at what is about to happen. Say you have a workload that runs a heavy batch job every Saturday night. Everpure Fusion knows that. It has seen the pattern. So when the placement engine is evaluating tier assignments, it will never recommend moving that workload to a lower-performance tier just because it looks quiet on a Tuesday afternoon. It understands what Tuesday quiet actually means in context. And if that workload somehow ends up on the wrong tier, perhaps through a manual change or a migration gone sideways, Everpure Fusion will proactively raise a violation before the weekend arrives. Not after the SLA is missed. Before. The cumulative effect is that customers can operate their fleets closer to full utilization without the anxiety that normally comes with it. Underused hardware gets activated, incremental purchases get deferred, and the reactive, always-behind-the-curve model of capacity management starts to look like a problem from a previous era. And Everpure Fusion does not stop at the infrastructure layer. Through its integration with Pure1 Application Intelligence, Everpure Fusion gains deeper visibility into the nature of the workloads themselves, not just how they behave, but what they actually are. That additional context means smarter decisions at every level, from initial placement to long-term tier management, grounded in a more complete picture of what your environment is really doing. Workload rebalance and mobility will be available towards the end of 2026. Compliance as part of the control plane Most storage compliance workflows follow the same pattern: an audit is announced, someone pulls reports from three different tools, cross-references configuration against a spreadsheet of expected settings, and spends two weeks proving that workloads are protected the way they're supposed to be. Then the audit ends and nothing changes until the next one. That model breaks at fleet scale. When you're managing dozens of arrays across multiple sites and protocols, manual audits don't just slow you down — they leave gaps that only get discovered at the worst possible time. Everpure Fusion Compliance is built into the control plane, not bolted on after provisioning. Because Everpure Fusion presets encode protection policies, replication requirements, SafeMode retention, and QoS settings at deployment time, Everpure Fusion always knows what every workload's intended configuration is. Drift detection is continuous — not periodic. When a workload deviates from its preset, Everpure Fusion instantly surfaces the violation — visible in the UI, queryable via API or CLI, and accessible to AI agents through an MCP server. Remediation can be triggered directly through the same interfaces, without pulling in a separate tool or writing a custom script. Fleet-wide compliance dashboards give storage admins a live view of posture across every array, with exportable audit-ready reports that don't require manual assembly. The shift is meaningful: compliance becomes a property of how the fleet operates, not a project that interrupts how the team works. Everpure Fusion Compliance will be available towards the end of 2026. From dashboards and scripts to natural-language fleet operations You know the drill. A latency spike hits production. You open three dashboards, run a handful of CLI queries, dig through alert logs, and piece together enough context to understand what happened — and by then, you've already spent 45 minutes on a problem that should have taken five. The issue isn't the tools. It's that the context your fleet holds is trapped across systems that don't talk to each other. Everpure Fusion MCP Server changes that. Built on the open Model Context Protocol standard, it connects any MCP-compatible AI assistant — Claude, ChatGPT, Copilot, or internal agents — directly to live Everpure Fusion fleet state. Arrays, workloads, capacity, performance metrics, alert history, configuration, and placement data are normalized into clean, structured JSON and made available to AI in real time, pulled directly from Everpure Fusion and Purity REST APIs. The result: instead of navigating dashboards and stitching together CLI output, you ask a question. "Which arrays are approaching capacity?" "What's driving latency on this workload?" "Which workloads are drifting from their preset?" Everpure Fusion MCP Server answers from live fleet context, not stale snapshots. This is the on-ramp to agentic storage operations. Everpure Fusion already enforces policy and placement across the fleet. Pure1 adds AI-driven analytics and recommendations on top. Together, they give infrastructure operators the foundation to move from reactive troubleshooting to intent-driven, increasingly autonomous fleet management. Using topology groups to encode real infrastructure boundaries If your Everpure Fusion fleet's topology model lives in a color-coded spreadsheet, three wikis, and the institutional memory of one senior admin who never takes vacation — this is for you. Everpure Fusion, built into Purity for FlashArray and FlashBlade, introduces Topology Groups: fleet-scoped objects that let you describe your arrays in the same language your architecture diagrams already use — regions, availability zones, datacenters, rows, racks. No more provisioning a Everpure Fusion workload and hoping it lands in the right building. A Everpure Fusion Topology Group is a hierarchical, tree-structured object. Groups nest up to 10 levels deep (global → us-east → az-us-east-1a → dc01 → row3 → rack12), each array belongs to exactly one parent, and cycles are rejected at write time. Critically, they encode placement semantics — not access control. RBAC stays in Pure1 Resource Groups; topology stays in Everpure Fusion topology. Once modeled, Everpure Fusion presets reference groups using <group>.arrays notation. Everpure Fusion intersects the preset's allowed arrays with the group's membership at placement time. If there's no overlap, Everpure Fusion provisioning fails fast with a clear error — not silently in the wrong zone. The Everpure Fusion CLI shorthand makes automation clean: purevol list --context az-us-east-1a.arrays Everpure Fusion membership changes propagate automatically across the fleet. You stop maintaining a second source of truth outside the control plane. Stop treating topology as tribal knowledge. With Everpure Fusion, make it a first-class part of the intelligent control plane. Extending the model to Kubernetes and virtualization Most infrastructure operators are managing two parallel storage worlds right now: traditional VMs and databases on one side, Kubernetes-based containerized workloads on the other. Separate toolchains. Separate provisioning workflows. Separate everything. Everpure Fusion changes that. Through the Portworx Fusion Controller, Everpure Fusion extends its policy and placement control plane directly into Kubernetes — without forcing developers to change their existing workflows. Everpure Fusion auto-discovers your FlashArray and FlashBlade fleet, then exposes Everpure Fusion presets as native Kubernetes StorageClasses. That means when a developer requests a persistent volume, Everpure Fusion's placement engine resolves it against your existing policy constraints — storage class, protection policy, topology group, replication requirements — the same way it does for any other Everpure Fusion workload. No separate control plane for modern environments. No array-by-array configuration for each cluster. New arrays added to the fleet are automatically discovered and configured, so the operational model stays consistent as infrastructure grows. For VMware environments, Everpure Fusion extends the same operational model through the Everpure Fusion vSphere plugin, connecting storage management directly into virtualization workflows instead of running it as a separate administrative domain. The result: one control plane, one set of policies, one placement engine — spanning VMs, containers, and databases across the fleet. That is fewer parallel stacks to operate, less configuration drift between environments, and a more scalable path to consistent storage operations across the full infrastructure stack. Everpure Fusion as the storage admin foundation for autonomous operations The through-line across everything covered in this blog is simple: Everpure Fusion gives infrastructure operators a unified, policy-driven control plane that enforces intent consistently — across provisioning, placement, compliance, topology, and now Kubernetes and virtualization. That foundation matters because autonomous storage operations do not start with AI. They start with structure. Topology groups encode where workloads belong. Presets encode how they should be configured. Everpure Fusion presets exposed as StorageClasses ensure Kubernetes environments follow the same rules as everything else. When that structure is in place, AI can recommend, optimize, and eventually act — because the context is already clean, trusted, and machine-readable. For storage admins, the shift is real: less time resolving incidents caused by placement decisions nobody remembers making, more time defining the intent that governs the fleet. Everpure Fusion is that foundation — built into Purity, not bolted on. Want to learn more about Everpure Fusion? Check out the following links to dive deeper: Join the Everpure Fusion Mastery Program to build expertise, complete hands-on activities and earn rewards. Sign up for a Fusion test drive to try it out on your own time. Check out more about Fusion product details. Watch our cool new Fusion demo videos. Read Everpure Fusion Datasheet
Replication Solves Distribution. Auditing Solves Trust.
Ever wonder how to get data exactly where it needs to go without sacrificing security? This post explores how FlashBlade uses replication and auditing to make your data both mobile and trustworthy, helping you tackle everything from complex AI workflows to meeting strict compliance rules.6 Surprising Truths About Object Storage
This short, easy-to-consume article explains that cloud storage—especially object storage—is not just a bigger version of traditional storage but a fundamentally different system built for massive scale. It highlights key concepts like abstraction (separating how data is accessed from how it’s stored), the illusion of folders in a flat storage structure, and the power of rich, customizable metadata that turns storage into a searchable, automated platform. It also covers how Amazon’s S3 API became the industry standard, why objects are immutable (requiring full replacements instead of edits), and how low storage costs can be offset by expensive data retrieval fees. Overall, these design choices make object storage the backbone of modern cloud applications and data-driven systems.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 | #dmitrywashereBoosting SQL Server Backup/Restore Performance: Threads and Parallelism
In this post, we’ll discuss day 1 tuning you can do on your database hosts to take full advantage of your new high-performance backup storage. We’ll go over a few tricks around database layout and backup configuration for maximum throughput, discuss some quirks with SMB, and finally discuss using S3 effectively.
