Ask Us Everything: Everpure Object — What You Need to Know
Why Object Exists (and Why It’s Different) Justin opened with a reset that resonated: file and object may both store unstructured data, but they are built on different assumptions. File storage evolved from human workflows — folders, directories, locking semantics, POSIX guarantees. That model works well for users and shared drives. But those same assumptions become friction at cloud scale. Object storage was built for machines. It uses a flat namespace, atomic operations, embedded metadata, and native versioning. That’s why modern applications — backup platforms, analytics engines, AI frameworks — increasingly request S3 buckets instead of file shares. It’s not that file storage is going away; it’s that machines prefer object. Scale: 3.8 Trillion Objects and Counting One of the standout moments was a validation that Everpure ran for a customer, which tested 3.8 trillion objects in a single bucket on FlashBlade. They didn’t stop because they hit a ceiling — they stopped because they ran out of time. That matters because unlimited scaling isn’t guaranteed in most on-prem object systems. Many legacy solutions quietly impose metadata or bucket limits that don’t surface until you’re deep into production. If your roadmap includes AI datasets, large backup repositories, analytics pipelines, or content delivery use cases, scale limits quickly become real-world constraints. Object for AI: Performance Has Changed the Conversation Using object for AI dominated the Q&A — and for good reason. Training workloads demand enormous throughput, especially for checkpointing bursts across large GPU clusters. Inference workloads are more latency-sensitive and read-heavy. FlashBlade’s architecture, including S3 over RDMA, separates metadata authentication from the data path and enables direct, high-throughput access to data nodes. The team referenced performance in the hundreds of GB/sec range on multi-chassis systems. Justin made an important observation: AI initially landed on file systems simply because object storage wasn’t considered performant enough. That assumption is changing rapidly. Object on FlashArray: The “Alongside Block” Story A lot of questions focused on object running on FlashArray — resiliency, performance expectations, and which workloads are a fit. Writes are acknowledged only after safe persistence, and standard object retry logic handles failure scenarios cleanly. So, you can be sure of data integrity, even if a controller fails. FlashArray Object is designed for smaller-scale S3 use cases: artifact repositories, container workloads, image stores, edge environments, and test/dev scenarios. FlashBlade remains the scale-out platform for massive object footprints. Over time, Everpure Fusion will increasingly abstract placement decisions so workloads land on the right platform without adding operational complexity. Data Reduction and Garbage Collection: The Hidden Advantages One of the more practical differentiators discussed was garbage collection. Many legacy object systems struggle with delete churn because of layered indirection — objects are marked, then nodes are marked, then underlying file systems are marked, then media eventually reclaims space. Because Everpure controls the stack end-to-end — logical object through physical media — reclamation is cohesive and efficient. Combined with always-on compression and similarity-based DeepReduce techniques, customers see meaningful space savings without sacrificing performance. Migration: It’s an Application Decision Perhaps the most important takeaway: moving from file to object isn’t a storage copy exercise. It’s an application transition. Backup software, artifact repositories, and analytics platforms increasingly support object natively. Let the application drive the migration instead of trying to brute-force a file-to-object copy. Object is growing quickly, but the shift doesn’t require abandoning everything at once. With FlashArray for edge and unified workloads, FlashBlade for scale-out performance, and Everpure Fusion tying it together, we are building a platform where object can grow naturally alongside block — not replace it overnight. If you have follow-up questions, bring them into the Pure Community. The conversation around object is only getting bigger.
Simplifying Observability: Native OpenTelemetry in Purity
As enterprises modernize and accelerate their infrastructure through automation, blind spots become more expensive. When systems move faster, teams need telemetry that’s reliable, portable, and easy to integrate across a heterogeneous stack. Pure Storage’s Enterprise Data Cloud vision reflects that shift: infrastructure that delivers cloud-like simplicity and speed while preserving the control, security, and performance enterprises expect. Fusion supports this by standardizing and scaling self-service workflows, turning storage into an on-demand platform. But faster operations require a stronger feedback loop. As automation increases, teams need confidence that systems remain healthy and predictable. That’s why consolidated observability is foundational. Instead of running separate monitoring tools per layer, organizations are centralizing telemetry into a single observability platform that can correlate signals end-to-end; from the end user’s experience (e.g. browser or mobile app), through the network and application code, all the way down to infrastructure like servers, databases, containers, and storage. This consolidation reduces redundant tools and fragmented dashboards while giving teams the correlated insights they need to resolve incidents faster and make better decisions. The Siloed Vendor Problem Yet achieving this unified vision has proven challenging. Traditional infrastructure vendors have long provided proprietary monitoring tools designed exclusively for their own products. A storage vendor offers one monitoring interface, the compute vendor another, and the network vendor yet another. Each tool uses different data formats, separate dashboards, and incompatible alerting mechanisms. For organizations running heterogeneous environments (which is nearly all of them), this creates an untenable situation. IT teams must context-switch between multiple tools, correlate data manually across platforms, and maintain expertise in numerous vendor-specific interfaces. When an application performance issue arises, determining whether the root cause lies in storage latency, network congestion, or compute resource exhaustion becomes an exercise in detective work across disconnected systems. The promise of consolidated observability cannot be realized with vendor-specific, siloed monitoring tools. A different approach is needed. The Open Standard Solution This challenge has driven the industry toward open, vendor-agnostic standards that enable telemetry interoperability. OpenMetrics emerged as one such standard, providing a common data model for exposing metrics (counters, gauges, and histograms) in a format that any observability platform can consume. By standardizing metric exposition, OpenMetrics reduced vendor lock-in and became foundational to Prometheus-based monitoring at scale. However, standardizing the format of metrics is only one part of what organizations need to make consolidated observability work in practice. Enterprises also need consistency in how telemetry is named, described, transported, and exported, so that infrastructure data can flow cleanly across heterogeneous environments without bespoke integrations. Enter OpenTelemetry, which expands on the same vendor-neutral principles to create a comprehensive observability framework. In other words, it helps ensure telemetry isn’t just emitted in a readable format, but is also structured and delivered in a way that remains portable across vendors and backends. Think of it as establishing the equivalent of a USB standard for telemetry data: any "device" (an application or infrastructure component) can plug into any "peripheral" (an observability platform) without requiring proprietary connectors. The primary benefit is profound: freedom from vendor lock-in. Organizations can choose best-of-breed observability platforms based on capabilities and cost rather than being constrained by what their infrastructure vendors support. The External Agent Bottleneck OpenTelemetry and OpenMetrics have made consolidated observability technically feasible, but most storage vendors have adopted these standards through what can only be described as a "bolt-on" approach. This forces customers to manage a complex chain of external agents, sidecars, or dedicated VMs, just to get telemetry from their platforms visualized onto their dashboards. This presents a problem that’s two-fold: Operational Overhead: Instead of simply consuming data, IT teams are burdened with sizing, patching, and troubleshooting the monitoring infrastructure itself. New Failure Modes: If an agent crashes or becomes misconfigured, visibility into critical infrastructure disappears precisely when it's needed most. Teams find themselves monitoring their monitoring infrastructure; a meta-problem that defeats the original purpose. The Native Integration Imperative In the Pure Storage platform, observability is a first-class capability instead of an afterthought. Thus, Pure Storage has taken a different path: an OpenTelemetry collector embedded into Purity OS. Instead of asking customers to deploy and maintain external agents, exporters, or intermediary infrastructure, Pure Storage platforms will now expose telemetry in standardized OpenTelemetry format as an intrinsic platform capability. The result is sending storage telemetry directly into any OpenTelemetry-compatible Observability platform-of-choice (eg., Datadog, Dynatrace, Splunk, Grafana, etc.). Fig. Numbers represent the sequence of steps in the workflow Pure Storage’s commitment has always been simplicity. Native OpenTelemetry in Purity OS extends that principle to observability: less integration friction, fewer moving parts, and more time spent acting on insight instead of maintaining the pipeline. More information on the native integration of OpenTelemetry Collector within Purity//FB can be found here. Purity//FA to follow soon.Ask Us Everything: Evergreen//One™ Edition — What the Community Learned
A recent Ask Us Everything (AUE) session on Pure Storage Evergreen//One™ was a lively, deeply technical conversation—and exactly the kind of dialogue that makes the Pure Community special. Here are some of the biggest takeaways, organized around the questions asked and the insights that followed.
Stop Prompting, Start Context Engineering
This blog post argues that Context Engineering is the critical new discipline for building autonomous, goal-driven AI agents. Since Large Language Models (LLMs) are stateless and forget information outside their immediate context window, Context Engineering focuses on assembling and managing the necessary information—such as session history, long-term memory (embeddings, RAG indexes), and tool outputs—for the agent every single turn. The post asserts that storage, not the LLM or the prompt, is the primary performance bottleneck for AI at scale. The speed of the underlying storage architecture dictates the agent's responsiveness because it must quickly retrieve and persist context data repeatedly.OT: The Architecture of Interoperability
In previous post, we explored the fundamental divide between Information Technology (IT) and Operational Technology (OT). We established that while IT manages data and applications, OT controls the physical heartbeat of our world from factory floors to water treatment plants. In this post we are diving deeper into the bridge that connects them: Interoperability. As Industry 4.0 and the Internet of Things (IoT) accelerate, the "air gap" that once separated these domains is evolving. For modern enterprises, the goal isn't just to have IT and OT coexist, but to have them communicate seamlessly. Whether the use-cases are security, real time quality control, or predictive maintenance, to name a few, this is why interoperability becomes the critical engine for operational excellence. The Interoperability Architecture Interoperability is more than just connecting cables; it’s about creating a unified architecture where data flows securely between the shop floor and the “top floor”. In legacy environments, OT systems (like SCADA and PLCs) often run on isolated, proprietary networks that don’t speak the same language as IT’s cloud-based analytics platforms. To bridge this, a robust interoperability architecture is required. This architecture must support: Industrial Data Lake: A single storage platform that can handle block, file, and object data is essential for bridging the gap between IT and OT. This unified approach prevents data silos by allowing proprietary OT sensor data to coexist on the same high-performance storage as IT applications (such as ERP and CRM). The benefit is the creation of a high-performance Industrial Data Lake, where OT and IT data from various sources can be streamed directly, minimizing the need for data movement, a critical efficiency gain. Real Time Analytics: OT sensors continuously monitor machine conditions including: vibration, temperature, and other critical parameters, generating real-time telemetry data. An interoperable architecture built on high performance flash storage enables instant processing of this data stream. By integrating IT analytics platforms with predictive algorithms, the system identifies anomalies before they escalate, accelerating maintenance response, optimizing operations, and streamlining exception handling. This approach reduces downtime, lowers maintenance costs, and extends overall asset life. Standards Based Design: As outlined in recent cybersecurity research, modern OT environments require datasets that correlate physical process data with network traffic logs to detect anomalies effectively. An interoperable architecture facilitates this by centralizing data for analysis without compromising the security posture. Also, IT/OT convergence requires a platform capable of securely managing OT data, often through IT standards. An API-First Design allows the entire platform to be built on robust APIs, enabling IT to easily integrate storage provisioning, monitoring, and data protection into standard, policy-driven IT automation tools (e.g., Kubernetes, orchestration software). Pure Storage addresses these interoperability requirements with the Purity operating environment, which abstracts the complexity of underlying hardware and provides a seamless, multiprotocol experience (NFS, SMB, S3, FC, iSCSI). This ensures that whether data originates from a robotic arm or a CRM application, it is stored, protected, and accessible through a single, unified data plane. Real-World Application: A Large Regional Water District Consider a large regional water district, a major provider serving millions of residents. In an environment like this, maintaining water quality and service reliability is a 24/7 mission-critical OT function. Its infrastructure relies on complex SCADA systems to monitor variables like flow rates, tank levels, and chemical compositions across hundreds of miles of pipelines and treatment facilities. By adopting an interoperable architecture, an organization like this can break down the silos between its operational data and its IT capabilities. Instead of SCADA data remaining locked in a control room, it can be securely replicated to IT environments for long-term trending and capacity planning. For instance, historical flow data combined with predictive analytics can help forecast demand spikes or identify aging infrastructure before a leak occurs. This convergence transforms raw operational data into actionable business intelligence, ensuring reliability for the communities they serve. Why We Champion Compliance and Governance Opening up OT systems to IT networks can introduce new risks. In the world of OT, "move fast and break things" is not an option; reliability and safety are paramount. This is why Pure Storage wraps interoperability in a framework of compliance and governance, not limited to: FIPS 140-2 Certification & Common Criteria: We utilize FIPS 140-2 certified encryption modules and have achieved Common Criteria certification. Data Sovereignty: Our architecture includes built-in governance features like Always-On Encryption and rapid data locking to ensure compliance with domestic and international regulations, protecting sensitive data regardless of where it resides. Compliance: Pure Fusion delivers policy defined storage provisioning, automating the deployment with specified requirements for tags, protection, and replication. By embedding these standards directly into the storage array, Pure Storage allows organizations to innovate with interoperability while maintaining the security posture that critical OT infrastructure demands. Next in the series: We will explore further into IT/OT interoperability and processing of data at the edge. Stay tuned!Understanding Deduplication Ratios
It’s super important to understand where deduplication ratios, in relation to backup applications and data storage, come from. Deduplication prevents the same data from being stored again, lowering the data storage footprint. In terms of hosting virtual environments, like FlashArray//X™ and FlashArray//C™, you can see tremendous amounts of native deduplication due to the repetitive nature of these environments. Backup applications and targets have a different makeup. Even still, deduplication ratios have long been a talking point in the data storage industry and continue to be a decision point and factor in buying cycles. Data Domain pioneered this tactic to overstate its effectiveness, leaving customers thinking the vendor’s appliance must have a magic wand to reduce data by 40:1. I wanted to take the time to explain how deduplication ratios are derived in this industry and the variables to look for in figuring out exactly what to expect in terms of deduplication and data footprint. Let’s look at a simple example of a data protection scenario. Example: A company has 100TB of assorted data it wants to protect with its backup application. The necessary and configured agents go about doing the intelligent data collection and send the data to the target. Initially, and typically, the application will leverage both software compression and deduplication. Compression by itself will almost always yield a decent amount of data reduction. In this example, we’ll assume 2:1, which would mean the first data set goes from 100TB to 50TB. Deduplication doesn’t usually do much data reduction on the first baseline backup. Sometimes there are some efficiencies, like the repetitive data in virtual machines, but for the sake of this generic example scenario, we’ll leave it at 50TB total. So, full backup 1 (baseline): 50TB Now, there are scheduled incremental backups that occur daily from Monday to Friday. Let’s say these daily changes are 1% of the aforementioned data set. Each day, then, there would be 1TB of additional data stored. 5 days at 1TB = 5TB. Let’s add the compression in to reduce that 2:1, and you have an additional 2.5TB added. 50TB baseline plus 2.5TB of unique blocks means a total of 52.5TB of data stored. Let’s check the deduplication rate now. 105TB/52.5TB = 2x You may ask: “Wait, that 2:1 is really just the compression? Where is the deduplication?” Great question and the reason why I’m writing this blog. Deduplication prevents the same data from being stored again. With a single full backup and incremental backups, you wouldn’t see much more than just the compression. Where deduplication measures impact is in the assumption that you would be sending duplicate data to your target. This is usually discussed as data under management. Data under management is the logical data footprint of your backup data, as if you were regularly backing up the entire data set, not just changes, without deduplication or compression. For example, let’s say we didn’t schedule incremental backups but scheduled full backups every day instead. Without compression/deduplication, the data load would be 100TB for the initial baseline and then the same 100TB plus the daily growth. Day 0 (baseline): 100TB Day 1 (baseline+changes): 101TB Day 2 (baseline+changes): 102TB Day 3 (baseline+changes): 103TB Day 4 (baseline+changes): 104TB Day 5 (baseline+changes): 105TB Total, if no compression/deduplication: 615TB This 615TB total is data under management. Now, if we looked at our actual, post-compression/post-dedupe number from before (52.5TB), we can figure out the deduplication impact: 615/52.5 = 11.714x Looking at this over a 30-day period, you can see how the dedupe ratios can get really aggressive. For example: 100TB x 30 days = 3,000TB + (1TB x 30 days) = 3,030TB 3,030TB/65TB (actual data stored) = 46.62x dedupe ratio In summary: 100TB, 1% change rate, 1 week: Full backup + daily incremental backups = 52.5TB stored, and a 2x DRR Full daily backups = 52.5TB stored, and an 11.7x DRR That is how deduplication ratios really work—it’s a fictional function of “what if dedupe didn’t exist, but you stored everything on the disk anyway” scenarios. They’re a math exercise, not a reality exercise. Front-end data size, daily change rate, and retention are the biggest variables to look at when sizing or understanding the expected data footprint and the related data reduction/deduplication impact. In our scenario, we’re looking at one particular data set. Most companies will have multiple data types, and there can be even greater redundancy when accounting for full backups across those as well. So while it matters, consider that a bonus.
Ask Us Everything Recap: Making Purity Upgrades Simple
At our recent Ask Us Everything session, we put a spotlight on something every storage admin has an opinion about: software upgrades. Traditionally, storage upgrades have been dreaded — late nights, service windows, and the fear of downtime. But as attendees quickly learned, Pure Storage Purity upgrades are designed to be a very different experience. Our panel of Pure Storage experts included our host Don Poorman, Technical Evangelist, and special guests Sean Kennedy and Rob Quast, Principal Technologists. Here are the questions that sparked the most conversation, and the insights our panel shared. “Are Purity upgrades really non-disruptive?” This one came up right away, and for good reason. Many admins have scars from upgrade events at other vendors. Pure experts emphasized that non-disruptive upgrades (NDUs) are the default. With thousands performed in the field — even for mission-critical applications — upgrades run safely in the background. Customers don’t need to schedule middle-of-the-night windows just to stay current. “Do I need to wait for a major release?” Attendees wanted to know how often they should upgrade, and whether “dot-zero” releases are safe. The advice: don’t wait too long. With Pure’s long-life releases (like Purity 6.9), you can stay current without chasing every new feature release. And because Purity upgrades are included in your Evergreen subscription, you’re not paying extra to get value — you just need to install the latest version. Session attendees found this slide helpful, illustrating the different kinds of Purity releases. “How do self-service upgrades work?” Admins were curious about how much they can do themselves versus involving Pure Storage support. The good news: self-service upgrades are straightforward through Pure1, but you’re never on your own. Pure Technical Services knows that you're running an upgrade, and if an issue arises you’re automatically moved to the front of the queue. If you want a co-pilot, then of course Pure Storage support can walk you through it live. Either way, the process is fast, repeatable, and built for confidence. Upgrading your Purity version has never been easier, now that Self Service Upgrades lets you modernize on your schedule. “Why should I upgrade regularly?” This is where the conversation shifted from fear to excitement. Staying current doesn’t just keep systems secure — it unlocks new capabilities like: Pure Fusion™: a unified, fleet-wide control plane for storage. FlashArray™ Files: modern file services, delivered from the same trusted platform. Ongoing performance, security, and automation enhancements that come with every release. One attendee summed it up perfectly: “Upgrading isn’t about fixing problems — it’s about getting new toys.” The Takeaway The biggest lesson from this session? Purity upgrades aren’t something to fear — they’re something to look forward to. They’re included with your Evergreen subscription, they don’t disrupt your environment, and they unlock powerful features that make storage easier to manage. So if you’ve been putting off your next upgrade, take a fresh look. Chances are, Fusion, Files, or another feature you’ve been waiting for is already there — you just need to turn it on. 👉 Want to keep the conversation going? Join the discussion in the Pure Community and share your own upgrade tips and stories. Be sure to join our next Ask Us Everything session, and catch up with past sessions here!Don’t Wait, Innovate: Long‑Life Release 6.9.0 Is Your Gateway to Continuous Innovation
How Pure Releases Work (and Why You Should Care) Pure Storage doesn’t make you choose between stability and innovation: Feature Releases arrive monthly and are supported for 9 months. They’re production‑ready and ideal if you like to live on the cutting edge. Long‑Life Releases (LLRs) bundle those feature releases into a thoroughly tested version which is supported for three years. LLR 6.9.0 is essentially all the innovation of those Feature releases, rolled into one update. This dual approach means you can adopt new features as soon as they’re ready or wait for the next stable release—either way, you keep moving forward. Not sure what features you’re missing? Not a problem as we have a tool for that. A coworker reminded me: Pure1’s AI Copilot can tell you exactly what you’ve been missing. Here’s how easy it is to find out: Log into Pure1, click on the AI Copilot tab, and type your question. My coworker reminded me of this last week, so I tried: “Please provide all features for FlashArray since version 6.4 of Purity OS.” Copilot returned a detailed rundown of new capabilities across each release. In just a couple of minutes, I saw everything I’d overlooked—no digging through release notes or calling support required. A Taste of What You’ve Been Missing Here’s a snapshot of the goodies you may have missed across the last few year releases: Platform enhancements: FlashArray//E platform (6.6.0) extends Pure’s simplicity to tier‑3 workloads. Gen 2 chassis support (6.8.0) delivers more performance and density with better efficiency. 150 TB DirectFlash modules (6.8.2) boost capacity without compromising speed. File services advancements: FlashArray File (GA in 6.8.2) lets you manage block and file workloads from the same array. SMB Continuous Availability shares (6.8.6) keep file services online through failures. Multi‑server/domain support (6.8.7) scales file services across larger environments. Security and protection: Enhanced SafeMode protection (6.4.3) quadruples local snapshot capacity and adds hardware tokens for instant data locking which is vital in a ransomware era. Over‑the‑wire encryption (6.6.7) secures asynchronous replication. Pure Fusion: We can't talk about this enough Think of this as fleet intelligence. Fusion applies your policies across every array and optimizes placement automatically, cutting operational overhead . Purity OS: It’s Not Just Firmware Every Purity OS update adds value to your existing hardware. Recent improvements include support for new NAND sources, “titanium” efficiency power supplies, and advanced diagnostics. These aren’t minor tweaks; they’re part of Pure’s Evergreen promise that your hardware investment keeps getting better over time. Why Waiting Doesn’t Pay Off It’s tempting to delay updates, but with Pure, waiting often means you’re missing out on: Security upgrades that counter new threats. Performance gains like NVMe/TCP support and ActiveCluster improvements. Operational efficiencies such as open metrics and better diagnostics. Future‑proofing features that prepare you for upcoming innovations. Your Roadmap to Capture These Benefits Assess your current state: Use AI Copilot to see exactly what you’d gain by moving to LLR 6.9.0. Plan your update: Pure’s non‑disruptive upgrades let you modernize without downtime. Explore new features: Dive into Fusion, enhanced file services, and expanded security capabilities. Connect with the community: Share experiences with other users to accelerate your learning curve. The Bottom Line Pure’s Evergreen model means your hardware doesn’t just retain value it continues to gain it. Long‑Life Release 6.9.0 is a gateway to innovation. In a world where data is your competitive edge, standing still is equivalent to moving backward. Ready to see what you’ve been missing? Log into Pure1, fire up Copilot, and let it show you the difference between where you are and where you could be.
