Coder Legion - Recent posts tagged cloudnative

Cloud-Native Enterprise Platforms: What Developers Need to Know

Thu, 24 Sep 2026 05:02:17 +0000

Enterprise software has evolved dramatically over the last decade. Applications that ran on local servers and supported a limited number of employees now need to support distributed teams, cloud infrastructure, third-party integrations, mobile users, growing datasets and constantly evolving business requirements.

This shift has made cloud-native enterprise platforms a core component of modern software architecture.

But cloud-native is more than simply lifting and shifting an existing application from a physical server to the cloud. That means rethinking how you design, deploy, integrate, secure, monitor, and scale applications.

For developers, it is becoming increasingly important to understand this shift as organizations move away from isolated business applications toward interconnected and adaptable technology ecosystems.

Why It’s Getting Harder to Support Legacy Business Applications

Many traditional enterprise applications were based on a fairly simple model. Users accessed a central application in a controlled environment, with much of the processing and data remaining within an organisation’s own infrastructure.

That model worked well when businesses had less locations, less integrations, and relatively predictable workloads.

Today’s organizations work differently.

An enterprise application may need to connect to customer relationship management systems, accounting software, HR software, analytical tools, databases, APIs, authentication services, and other third-party applications. Employees can also access these systems from offices, homes, mobile devices and other geographic locations.

In tightly coupled applications even a small change can impact many parts of the system. Testing is more complicated, deployments are slower, and it becomes difficult to scale a particular function independently.

Developers have responded by looking into more modular and flexible ways of developing enterprise applications.

What is Cloud Native?

Cloud-native is not just hosting an application on AWS, Microsoft Azure, Google Cloud, or any other cloud provider.

The idea is wider.

Cloud native development is about building applications to take advantage of elasticity, automation, distributed infrastructure, resilience and continuous delivery.

Depending on the requirements, a cloud-native application might use containers, APIs, automated deployment pipelines, independent services, infrastructure automation, centralized logging, monitoring and elastic resource allocation.

But there is no one architecture which all organizations should follow.

But a business with a relatively simple app may not need dozens of microservices. In some cases a well-designed monolithic application can still be easier to operate and maintain.

We should be looking for an architecture that fits the requirements of the application, not for popular technologies.

A modular architecture makes change simple

Modularity is one of the central ideas in modern enterprise architecture.

For example, a business application that manages finance, human resources, inventory, customer management, reporting and document processing.

All these functions are tightly coupled so changing a part of the application might cause unanticipated effects somewhere else.

The modular architecture has clearer boundaries to the different responsibilities.

Developers are able to change individual components and not have to touch the entire application. Also, the testing can be more focused as individual components can be tested separately.

Modularity does not eliminate complexity. Distributed applications suffer from problems such as communication failures, data consistency, authentication, service discovery, and monitoring.

The value of modular architecture is thus a function of the quality of these boundaries.

APIs Connect the Enterprise Software Ecosystem

Today, very few organizations rely on a single software application.

Companies typically have many different systems, each designed to do a different job.

APIs offer a way for these systems to interact without forcing developers to duplicate functionality or reveal the internal implementation of an application.

For instance, an enterprise application might need to talk to a payroll system via an API instead of employees manually moving the data between the two systems.

This can reduce the need for repetitive work and help to create more consistent data flows.

Thinking in an API-first way also changes the way applications are designed for developers. The interface between systems becomes an explicit contract that can be documented, tested, versioned and maintained.

Scalability is more than simply the number of users

Scalability is frequently defined as the ability of an application to deal with more users or requests.Enterprise scalability is more complex.

Seasonal demand, sudden increase in transactions, expansion into new markets, other departments or rapidly growing data volumes can be a business reality.

Cloud-native architectures can provide resources that can be resized based on workload.

For example, a reporting or analytics component may use much more computing resources during a reporting cycle than during normal operations.

If components can scale independently, organizations don’t have to scale up the entire application just because one function has become more demanding.

This approach can enable more flexible resource management, but also calls for careful architectural planning.

Security Must Be Part of the Architecture

Cloud environments also change how developers need to think about securing applications.

Security is not a bolt-on feature!

Modern enterprise applications need to consider authentication and authorization, identity management, encryption, API security, control of access, audit trails, data protection and monitoring.

For example, role-based access control can limit the function and information available to users based on their responsibilities.

Communication between different services need some attention as well. Each API, integration, and external connection is another component that needs to be authenticated and access controlled correctly. This means that security needs to be thought of across the whole lifecycle of development and deployment in a cloud-native architecture.

Observability is now required

As applications get more distributed understanding what is going on inside of them is getting harder and harder.

Traditionally, application troubleshooting has been a case of looking at one server and its logs.

In a distributed environment, a single user request may pass through several services before generating a response.

This is why observability is so important.

Logs tell us about events, metrics tell us about changes in system behaviour, and traces help developers follow the requests across multiple services.

Monitoring and alerts can also help teams catch problems before they turn into larger operational issues.

It is not enough to know that something has gone wrong. Developers need sufficient information to know where and why the failure happened.

How Cloud-Native Development Is Changing the Game for Developers

Cloud native architecture is also changing the skills expected of modern developers.

While writing application code is still at the heart of software development, developers are increasingly required to understand what is going on around that code.

Developers who are familiar with APIs, databases, cloud infrastructure, containers, CI/CD pipelines, authentication, observability, security, and distributed systems can make better architectural decisions. That's not to say every developer needs to be a cloud architect or infrastructure specialist.

What that means is that the line between application development and infrastructure is starting to blur.

Developers today are expected to know how their applications are deployed, monitored, secured and scaled.

Microservices Aren't Always the Answer

The popularity of microservices has led some organizations to believe that simply breaking an application into many independent services makes it more modern or scalable.

That is not always so.

Microservices have the potential to provide benefits such as independent deployment and service level scalability, but also introduce operational complexity.

They have to take care of service communication, distributed data, monitoring, deployment, security and failure management.

For a small or medium complexity application these issues may outweigh the benefits.

The question is not whether an application should use microservices or not, but rather whether the architecture enables the organization to make changes effectively, while ensuring reliability and operational control.

The Future of Enterprise Software Is Getting More Connected

Enterprise applications are being integrated into broader ecosystems of technology, not as stand-alone systems.

Businesses need software that can talk to other applications, react to new demands, accommodate distributed users and deal with variable workloads.

Cloud-native architecture is one approach to this kind of flexibility.

But the technology itself is only half the story. A poorly designed distributed system can be a lot harder to maintain than a well -designed monolith.

Containers and microservices and APIs and cloud infrastructure don’t magically create better software. The real goal is to build systems that can be maintained, that are secure, observable and resilient, and that can evolve with the organization.

How Kubernetes Networking Works | Pods, Services & DNS Explained for Beginners

Mon, 21 Sep 2026 12:24:10 +0000

A new video is out!

I’m explaining Kubernetes networking in my little way, especially how Pods, Services, and DNS work together.

If you’re learning Kubernetes and networking has been confusing, this one is for you.

How Kubernetes Networking Works | Pods, Services & DNS Explained for Beginners

Watch the full video here

Kubernetes #DevOps #CloudNative #TechCommunity

How Kubernetes Works | Kubernetes Explained Simply for Beginners

Mon, 14 Sep 2026 14:41:36 +0000

Kubernetes can feel confusing when you first start learning it.

Pods. Nodes. Services. Deployments. Scaling.

There are so many new terms that it is easy to focus on memorizing definitions instead of understanding how everything connects.

I recently created a simple animated explanation of how Kubernetes works, using a small application and a real-world-style scenario to walk through the concepts.

If you're currently learning Kubernetes, this might help make the pieces click.

What Kubernetes concept took you the longest to understand?

Kubernetes #DevOps #CloudComputing #K8s #CNCF #LFX

How a Football Match Helped Me Understand Kubernetes: Desired State vs Actual State

Sat, 18 Jul 2026 10:00:00 +0000

Have you ever wondered what Desired State and Actual State mean in Kubernetes?

I used to hear those terms all the time, but they didn't truly make sense until I watched a football match.

In this video, Ada explains one of Kubernetes' most important concepts using a simple football analogy. Instead of complicated diagrams and technical jargon, you'll see how a football coach, substitutions, and an unexpected injury perfectly explain how Kubernetes constantly works to keep your applications running.

Whether you're preparing for the KCNA, CKA, or CKAD, learning Kubernetes for DevOps, or just getting started with cloud-native technologies, this real-world story will help you understand one of Kubernetes' core ideas.

What you'll learn

What Desired State means in Kubernetes
What Actual State means
How Kubernetes compares both states
What reconciliation is
Why Kubernetes automatically creates new Pods
Why this makes Kubernetes reliable

🎯 Watch next

What a Busy Restaurant Can Teach You About Kubernetes Services

🚀 Want to practice Kubernetes yourself?

Spin up a managed Kubernetes cluster on DigitalOcean

You can follow my work and projects on Twitter, LinkedIn, GitHub, and YouTube, where I share what I am building and learning along the way.

If this video helped you understand Kubernetes better, consider subscribing for more real-life cloud and DevOps stories.

Thank you for reading.

The 88-Terabyte Heist By Google?

Thu, 09 Jul 2026 13:08:03 +0000

https://dev.to/neoncyan0x/the-88-terabyte-heist-2gmd

cloud_computing

The 88-Terabyte Heist

Thu, 09 Jul 2026 13:07:21 +0000

https://dev.to/neoncyan0x/the-88-terabyte-heist-2gmd

cloud_computing

Learning Cloud My Own Way

Wed, 08 Jul 2026 10:00:00 +0000

Disclosure: Some of the links in this article are affiliate links. This means I may earn a small commission if you click on them. I only recommend tools that I have used.

Learning cloud and DevOps takes patience, discipline, and consistency. There are days when everything clicks, and there are days when nothing seems to make sense. That is completely normal.

One thing I have discovered is that everyone learns differently.

Some people love following a detailed roadmap from beginning to end. Others prefer structured courses with a fixed schedule. For me, learning is more spontaneous. I follow my curiosity. When I find a topic interesting, I dive deep into it. No amount of advice or motivation can make me learn something if I genuinely have no interest in it.

Recently, I have been learning Kubernetes, and it has been one of the most interesting technologies I have studied so far. Instead of only reading documentation or watching tutorials, I decided to explain what I was learning by creating videos.

Here are two videos I created while learning Kubernetes:

Tools That Have Helped Me Learn

Along the way, I have used a few tools that have made learning easier.

DigitalOcean has been a great platform for experimenting with cloud services. If you are a startup or someone learning cloud on a budget, it is worth checking out because getting basic services running is straightforward.

I have also used Gamma to create presentations and portfolio content, and Lovable to build portfolio projects more quickly.

If you would like to explore these tools, here are the links:

Do You Really Need AWS to Learn Cloud?

One question I see often is whether you need AWS before you can start learning cloud.

My answer might surprise you.

Other cloud providers are friendly and can help you build practical skills before moving into AWS, like DigitalOcean.

There is no single right way to learn cloud computing.

Find a learning style that works for you.

If you enjoy reflections like this, you can subscribe to the newsletter.

You can follow my work and projects on Twitter, LinkedIn, GitHub, and YouTube, where I share what I am building and learning along the way.

Thank you for reading.

The 2026 Developer's Guide to Leveling Up — 5 Platforms Worth Your Time

Thu, 02 Jul 2026 11:00:37 +0000

If you're a working developer (or trying to become one), the hardest part of upskilling isn't motivation — it's picking where to actually spend your hours. Here's a rundown of five platforms, from full-stack fundamentals to cloud-native certification to career-launch programs.
Start with the fundamentals if you're building your foundation: TheDevSpace lays out a complete path from HTML and JavaScript through Node.js/Express on the backend and React/Next.js on the front.

What makes it useful for working devs is the code playground (no local setup needed) and the 12 real, source-code-included projects designed for a portfolio — plus a Next.js SaaS boilerplate if you're trying to ship something of your own in 2026.
Once fundamentals are solid, Zenva Academy is worth a serious look for specialization.

With 400+ courses spanning game engines (Godot, Unity, Unreal), core languages (Python, C#, C++), and — increasingly relevant this year — dedicated agentic AI coding and AI-app-building tracks, it's built for developers who learn by shipping projects rather than watching theory.
For devs who are earlier in their career and need structure and accountability, KodNest focuses on placement outcomes rather than just content.

Its Java, Python, Data Science, and GenAI tracks are built around real projects and interview-ready coaching for people trying to land their first engineering role.
If you're in the UK and want paid, structured, employer-matched training instead of self-study, NowSkills Limited runs government-funded digital and IT apprenticeships — a genuinely different model from online courses, connecting learners directly with employers since 2013.

And if you've got kids showing early interest in programming, ProgKids is worth bookmarking — 1:1 online lessons for ages 5–17 using Minecraft, Roblox, and Python, taught live rather than through pre-recorded content.

Whatever stage you're at, 2026 rewards developers who keep building — not just watching tutorials.

The 4 Signals That Actually Predict Production Failures - Part 1

Wed, 08 Apr 2026 16:28:22 +0000

Monitoring is not a dashboard only problem

In the last couple of years, I have moved across a few product teams. Every time I walk into an engineering team and ask how monitoring works. I get a standard response.

There is a dashboard

Usually, Grafana. Sometimes Kibana or Splunk. Team members have passed me links buried in wiki pages and nobody updates them anymore.

But dashboards don't make a system observable. I have worked on large scale cloud projects. All of them have dashboards but still struggle to answer basic questions during an incident.

What can fail?

The answer to this question can change how you think about monitoring. To me, monitoring is a system thinking problem. Any cloud solution that I work on is not a single application anymore. With cloud solutions, I am talking about API Gateways, event streams, microservices, containers, managed cloud services, and external integrations.

Something is always lagging somewhere, and all users are going to tell you is "The system is slow".

It might mean that either an API timing out or queue is backing up, or a container restarting or network path degraded.

After years of working on enterprise solutions, I can tell you that bugs do not cause major production incidents. They are often caused by lack of visibility, meaning missing the right metrics.

In this article, I try to put down some of the monitoring metrics that I find useful. These can help you diagnose incidents and keep the system reliable.

Monitoring vs Observability – Why it matters?

I can say that monitoring used to mean one thing:

Is the server alive?

If CPU was below 80% and memory was below 70%, everything looked fine.

Modern systems are not the same. You can have a healthy-looking infrastructure, a normal CPU utilisation, a healthy memory graph and can still have a production outage. I have seen services returning timeouts for hours while dashboards showed everything green. That can happen as traditional monitoring focuses on resources while failures can occur during interactions between services.

This is where observability comes in. Monitoring answers: "Is something wrong?" and observability answers: "Why is it wrong?" You need both.

What all you need?

In practice, you need three things. Metrics are used to detect problem, logs to explain errors and traces to discover latency. If your metrics are wrong, you would never know something is failing. And if you don't know something is failing, you never check logs and traces. Which is why metrics are the entry point of any investigation.

Typical monitoring challenges

Most of the time, teams don't have strategies for monitoring. It is the last backlog item to be picked up before the final production release. One service team adds a dashboard, another adds alerts and a third team introduces a different naming convention.

Six months down the line, you get duplicate metrics and inconsistent naming. There are no standard dashboards and alerts that nobody trusts. Eventually, teams ignore alerts, stop relying on monitoring and fall back to guesswork. That is a dangerous place to be.

One pattern I have seen repeatedly is metric explosion without clarity. A service exposes 400 metrics, and nobody knows which one matters.

Good monitoring is not about collecting more metrics. It is about collecting the right metrics. A production-ready service rarely needs more than 10–20 core metrics and a small number of critical alerts. Everything else is investigation detail. Not operational signal.

Four signals that every service needs

I recommend every service must expose below four signals. I sometimes refer to them as minimum survival metrics.

Latency
Traffic
Errors
Saturation

They can help you diagnose most production incidents. Let us discuss them.

Latency – Earliest signal

Latency is usually the earliest signal that something is going wrong. Long before error rates increase… Long before services fail… Latency begins to drift upward.

Slow systems become broken systems eventually. If you only monitor errors, you will detect problems late. If you monitor latency, you can catch problems early.

Why averages are false indicators?

Average latency is one of the most misleading metrics in production systems, however, many teams are still monitoring it. Consider a scenario where 990 requests complete in 40ms and 10 requests take 4 seconds. It means an average latency of about 80 ms. But a user experiencing the slow requests would strongly disagree.

Observing percentiles can solve this problem. You should track all below:

p50 for a typical user experience
p95 for degraded experience, and
p99 for worst-case experience

If p99 starts moving, it is the first clue during the incidents. Even if averages look perfect. Below is a code snippet to produce percentile metrics.

@RestController
public class OrderController {
    private final Timer orderTimer;

    public OrderController(MeterRegistry registry) {
        this.orderTimer =
            registry.timer("order.api.latency");
    }

    @GetMapping("/orders/{id}")
    public Order getOrder(@PathVariable String id) {
        return orderTimer.record(() -> {
            return orderService.getOrder(id);
        });
    }
}

What good latency dashboards look like?

A good latency dashboard should answer one question instantly

Are users experiencing slow responses?

That means, you are looking for percentiles and not averages with clear trends. If you need five minutes to interpret a graph, the dashboard is not working.

Traffic — System load

Traffic on your services tells you what the system is dealing with. Without traffic metrics, you cannot interpret latency or errors correctly. An error spike during a traffic spike means something very different from an error spike during normal load.

Traffic metrics include requests per second or events per second or messages per second or also batch rates. Most incidents begin with a traffic change. Sometimes expected and sometimes not.

A common pattern that I have always observed – Traffic increases and that increases latency. Integrations slow down and errors appear. Without traffic metrics, the root cause looks mysterious. With traffic metrics, it becomes obvious.

Prometheus query example

Requests per second:

rate(http_server_requests_seconds_count[1m])

This metric alone explains a surprising number of incidents.

Production incident

I once observed a latency spike that looked like a system failure. When I checked, CPU and memory looked normal, pods were healthy, error rates were low, and everything looked fine except traffic.

Traffic had doubled within ten minutes because of a partner integration retry loop. Nothing was technically broken, but the system was overloaded. Without traffic metrics, I would have spent hours investigating. That is the difference good monitoring makes.

I will continue this discussion in my next blog post.

How InfoScale Delivers Real-Time Resilience Across Hybrid Infrastructure

Thu, 29 Jan 2026 00:47:48 +0000

InfoScale showed up at the 66th IT Press Tour with a clear message: enterprise resilience needs to move beyond infrastructure-only approaches. The company, now part of Cloud Software Group, has been protecting mission-critical applications since its Veritas days, and they're addressing a problem that's getting worse, not better.

The Reality of Modern Application Stacks

Most enterprises run over 1,000 applications across different platforms. InfoScale says half of these are mission-critical. When you look at the technology stack involved—Oracle, AWS, Kubernetes, VMware, SQL Server, SAP, and dozens more—it's clear why traditional resilience approaches fall short.

The numbers tell the story. According to InfoScale's data, 59% of Fortune 500 companies experience an average of 1.6 hours of unexpected downtime per week. That adds up to 83 hours per year. For financial services, 24 hours of downtime costs $163 million, or $6.48 million per hour. And that's before you count regulatory fines.

Where Traditional Approaches Break

Joe D'Angelo, InfoScale's Director of Product Management, outlined four core challenges:

Operational blind spots. Thousands of interconnected systems create complexity that's hard to track. Most monitoring tools are reactive rather than predictive.

Constant change. Patching, scaling, and upgrades cause most downtime, but existing tools only respond after problems occur.

Infrastructure-only resilience. Protection, availability, and recovery happen in silos. This makes it difficult to ensure data remains resilient across applications and environments.

AI and cyber threats. AI pipelines amplify the cost of failure. Risk now occurs at any layer—application, data, infrastructure, or service.

How InfoScale Works

InfoScale provides software-defined resilience that sits between your applications and infrastructure. Here's the architecture:

At the bottom layer, it abstracts storage from any vendor—Dell EMC, NetApp, Pure Storage, HPE, IBM, Nutanix, AWS, Azure. You get block, file, and object access through a unified interface.

The middle layer handles data resiliency services: mirroring, snapshots, and replication. This works across any storage topology and includes application-consistent copies. The system monitors events and manages configuration across your entire environment.

At the application layer, InfoScale provides clustering with parallel access and instant failover. It integrates deeply with enterprise applications through specific agents for Oracle, SQL Server, SAP, MySQL, and others.

The orchestration layer manages failover, disaster recovery, and what InfoScale calls "fire drills"—automated testing of your recovery procedures.

Platform Support

InfoScale runs across:

Hypervisors: VMware, Hyper-V, KVM
Cloud: AWS, Azure, Google Cloud, Oracle Cloud
Container platforms: Kubernetes, OpenShift, Docker
Operating systems: Red Hat, SUSE, Oracle Linux, AIX, HP-UX, Windows
Storage: 1,000+ supported targets

This platform-agnostic approach matters. You don't have to rearchitect your entire stack to get resilience. InfoScale works with what you already have.

Real-Time Protection

InfoScale's value comes from operating in real-time across the full stack. When something fails, the system detects it at the application level, not just at the infrastructure level.

For cyber resilience, InfoScale offers secure snapshots with WORM (write once, read many) capability. If ransomware hits, you can roll back to a clean state. The system maintains data integrity through the entire process.

For modernization, InfoScale provides sequencing for complex application dependencies and rehearsals for migration testing. You can validate your procedures without disrupting production.

For hybrid cloud, the platform enables non-disruptive migration and repatriation. Move workloads between on-premises and cloud environments without downtime.

Performance Impact

InfoScale reports these results from customer deployments:

98% reduction in downtime (from 4 hours to 5 minutes)
320,000 IT hours saved per year on downtime support
Near-zero RPO (recovery point objective)
Near-zero RTO (recovery time objective)
Sub-60-second recovery for containerized applications

Red Hat Certification for OpenShift

For teams running Red Hat OpenShift, InfoScale offers certified resilience for both containers and virtualization. The platform provides:

Resilience built around the application, not just the infrastructure
Consistency across VMs and containers
Backend storage for live migration, parallel access, snapshots, and disaster recovery
Automated restarts, failovers, and scaling

This matters for organizations consolidating workloads on OpenShift. You get the same resilience model whether you're running traditional VMs or cloud-native containers.

AI and Autonomous Operations

InfoScale is working on autonomous resilience features powered by AI:

Predictive analytics for infrastructure failure forecasting
Intelligent workload optimization based on learned I/O patterns
Optimized recovery architecture with adaptive redundancy models
Workload fault simulations for predictive failure scenarios
Contextual alerting with application-aware operations
Proactive threat protection with attack simulations and anomaly detection

These capabilities are still in development. The goal is to reduce manual intervention and predict problems before they cause downtime.

Who Uses InfoScale

The customer list includes:

9 of 10 largest energy companies
10 of 10 largest telecom companies
10 of 10 largest financial services companies
9 of 10 largest pharmaceutical companies
7 of 10 largest healthcare companies
9 of 10 largest insurance companies

InfoScale serves 1,700+ enterprise customers across 52+ countries.

What This Means for Development Teams

For developers and DevOps teams, InfoScale addresses a specific problem: ensuring your applications stay available regardless of infrastructure failures.

You don't have to build resilience into every application. InfoScale provides it as a layer beneath your applications. This works whether you're running legacy apps or cloud-native workloads.

The platform integrates with your existing CI/CD pipeline and infrastructure-as-code tools. You can automate resilience policies and test them regularly without manual intervention.

For organizations moving to Kubernetes, InfoScale provides persistent volume management with built-in resilience. Your stateful applications get the same protection as traditional workloads.

Implementation Considerations

InfoScale isn't a point-and-click solution. It requires planning and configuration. You need to understand your application dependencies, define your resilience policies, and test your recovery procedures.

The platform works best when you treat resilience as code. Define your policies in configuration files, version them, and test them regularly. InfoScale provides the APIs and management interfaces to support this approach.

For new deployments, start with your most critical applications. Define your RPO and RTO requirements, then configure InfoScale to meet them. Use the fire drill feature to validate your setup before you need it.

Compliance and Regulations

InfoScale helps with DORA (Digital Operational Resilience Act) compliance. DORA, which started enforcement in January 2025, requires financial services organizations to implement standards for application and infrastructure resilience.

The platform provides the reporting and audit capabilities needed to demonstrate compliance. It tracks resilience metrics, recovery times, and test results.

Cost Model

InfoScale uses subscription licensing based on the number of protected instances. Pricing varies by platform, edition, and support level. The company offers both perpetual licenses and subscription models.

For organizations comparing build-versus-buy, InfoScale's value comes from covering multiple use cases: high availability, disaster recovery, data protection, and cloud migration. Building equivalent functionality in-house would require significant development resources.

Bottom Line

InfoScale provides resilience as a platform service. It works across your existing infrastructure and applications without requiring rearchitecture. The company has 30+ years of experience protecting enterprise applications, and that knowledge shows in the product.

If you're responsible for keeping applications running, InfoScale offers a way to do it without building custom solutions for every platform. The real-time detection and automated response reduce the manual work involved in maintaining availability.

The platform isn't simple to implement, but it solves a real problem. Downtime is expensive. InfoScale reduces it.

Dynatrace Perform 2026: What Developers Need to Know About the Platform's Latest Updates

Wed, 28 Jan 2026 17:42:45 +0000

Dynatrace used Perform 2026 to announce several updates that directly affect how developers work. The changes focus on reducing debugging time, adding AI-powered automation, and connecting frontend and backend observability in ways that weren't possible before.

What Changed

The company introduced Dynatrace Intelligence, which combines two types of AI. The first is deterministic AI - it uses real-time data about how your systems actually behave to find problems. The second is agentic AI - it can reason through issues and take action automatically within the guardrails you set.

This matters because it addresses a real problem. When you deploy AI-powered features or agentic workflows, they behave unpredictably. Traditional monitoring tools show you metrics, but they can't tell you why an LLM response took 8 seconds instead of 2, or why a specific user session failed.

Andreas Grabner, Fellow of Developer Advocate at Dynatrace, explains: "As organizations adopt AI at scale, as they start to build AI-native services, understanding how those systems behave in real environments becomes increasingly important. When frontend, backend, cloud, workflow and AI telemetry are connected through a single causal view, teams can see how user experience, application behavior, infrastructure, AI models and agentic workflows influence one another."

Real User Monitoring Gets an Upgrade

The new RUM capabilities address specific problems with modern web apps. Single-page applications, soft navigations, and asynchronous rendering create blind spots in traditional RUM tools. You see the metrics, but you can't connect frontend errors to backend traces or understand why users abandoned a session.

Dynatrace now stores RUM data in Grail, their data lakehouse, alongside logs, metrics, and traces. This means you can query frontend performance in the context of everything else. The Error Inspector app groups related errors and shows you the full context - not just the stack trace, but the user journey, backend services involved, and infrastructure state.

Dana Harrison at TELUS says developers there spend significantly less time debugging thanks to these tools. The company reduced deployment time for end-to-end observability from 600 minutes to 20 minutes using automation and monitoring as code.

IDE Integration and Live Debugging

Dynatrace expanded IDE support to include Windsurf and Cursor. Live Debugger now works directly in your programming environment, so you don't need to context-switch between your code and a separate observability platform.

This also includes mobile debugging. The new ANR (Application Not Responding) and crash diagnostics give you the context you need to fix mobile issues faster. Instead of generic crash reports, you get symbolized stack traces with full observability context.

Multi-Cloud Integrations

The platform now has deeper integrations with AWS, Azure, and Google Cloud. This matters if you're running services across multiple clouds or using cloud-native services like Azure AI Foundry or AWS Bedrock.

The integrations surface health indicators and automated alerts for cloud-native workloads. More importantly, they connect cloud infrastructure state to application behavior. When something goes wrong, you can see if it's your code, the cloud service, or how they're interacting.

Agentic Workflows and MCP Support

Dynatrace added support for the Model Context Protocol (MCP) and built agentic workflows into the platform. This means AI agents - whether from Claude, AWS Bedrock AgentCore, or Azure AI Foundry - can safely act on observability data.

The company also announced domain-specific agents for SRE, development, and security teams. These agents can triage issues, generate remediation plans, and coordinate with external systems like ServiceNow or GitHub.

When Dynatrace benchmarked an external SRE agent working with their deterministic agents, problems were solved up to 12 times more often, three times faster, and at half the cost compared to tests without deterministic agents.

DevCycle Acquisition

Dynatrace acquired DevCycle, a feature flag and experimentation platform. This adds runtime control at the feature level. You can validate behavior continuously and automatically respond to issues without redeploying.

The integration work is underway, with more updates coming as the acquisition progresses.

What This Means for Your Work

These changes address three common pain points:

Debugging AI features: You can now trace LLM calls, token usage, and model behavior alongside application code.
Frontend-backend correlation: No more guessing which backend service caused a frontend error. The full trace is connected.
Reducing manual work: Agents handle repeatable tasks like incident triage, vulnerability assessment, and performance optimization.

The platform aims to turn observability from a passive system that shows you problems into an active system that helps you fix them. Whether that saves you time depends on your specific workflow, but the capabilities are there.

Availability

Most features are available now. Azure and GCP integrations are in preview. DevCycle integration details will come later as the acquisition work continues.

If you're already using Dynatrace, these updates roll out through the normal update process. If you're evaluating observability platforms, the company offers a 15-day trial.

Modern systems generate petabytes of telemetry data, but most teams are still guessing what matters

Wed, 22 Oct 2025 01:05:56 +0000

Why Metrics Might Be Holding Your Observability Strategy Back

Metrics have been the foundation of observability for decades. But what if that foundation is cracked?

Ang Li, director of engineering at Observe, argues that metrics are a relic of outdated infrastructure constraints. They were necessary when systems couldn't handle large volumes of log data. Now, they might be doing more harm than good.

"Metrics force you to predefine what you think will matter before an incident even happens," Li explains. "If you guess wrong, you're effectively blind."

The Problem with Predicting Problems

The core issue isn't just philosophical. It's architectural.

Metrics require teams to decide upfront which labels, aggregation windows, and dimensions matter. But modern distributed systems are unpredictable. A metric might tell you API latency spiked, but it won't tell you the spike only affected requests from a specific region with a particular data pattern.

That context gets stripped during roll up. The user, request, payload, deployment, or dependency that triggered the failure disappears. You're left with a number that says something is wrong, but not why.

Li recalls troubleshooting out-of-memory errors at Observe. Engineers stared at pages of garbage collection metrics without clear insight. "Having individual request traces and logs is still critical to identify the culprit requests that brought down the system," he says.

How Cloud Economics Changed the Game

The technical landscape has shifted dramatically. Cloud infrastructure now makes it economically viable to store and analyze raw telemetry data at scale.

Observe's architecture centers on treating all telemetry as structured event data. They use Amazon S3 for storage with 10x compression, providing low-cost long-term retention for petabytes of data. Above that sits what they call the O11y Knowledge Graph, which transforms raw data into recognizable entities like customers, shopping carts, pods, and containers.

This approach required solving three key engineering challenges: scalable ingestion that adjusts to dynamic volume peaks, flexible transformation pipelines that model different system aspects, and intelligent query parallelization that handles 10GB the same way it handles 10TB.

"The customer should not care whether they are querying 10GB of data or 10TB," Li says. "The results should return in similar time frames."

Making Raw Data Analysis Viable

Processing raw telemetry instead of pre-aggregated metrics sounds computationally expensive. But several optimizations make it practical.

Columnar databases like Snowflake enable higher compression ratios than row-based systems. This reduces both storage costs and query scanning overhead. Observe also skips indexing by default, which is why search-based tools like Splunk become prohibitively expensive.

Aggressive query caching reuses existing results. Dashboard refreshes only re-query the changed portions, saving significant computational costs.

The Role of Metrics Going Forward

This isn't about eliminating metrics entirely. They still have value for infrastructure-level monitoring where detailed information is unavailable, like CPU utilization and OS metrics. They're efficient for capacity planning and catching broad systemic issues.

The shift happens when you need to understand why something broke in complex distributed systems.

"The real answer is combining metrics for high-level indicators and observability for deep contextual investigation," Li explains. "You need to recognize where you need richer data to maintain reliability at scale."

What This Means for Engineering Teams

Moving from metrics-first to direct data analysis changes daily workflows. Instead of jumping between dashboards and guessing which metric might be relevant, engineers can start with the actual data behind the behavior.

This makes troubleshooting more investigative and less reactive. Teams spend less time maintaining dashboards and more time understanding system behavior.

But it requires discipline. Raw data collection demands good instrumentation practices. Schema drifts can break downstream pipelines or alerting rules. Important log and trace messages need protection against accidental changes through unit tests.

AI as the Investigative Partner

AI is becoming crucial for navigating raw telemetry data. But not in the way you might expect.

"The power of AI lies in the capability to explore the data in steps to narrow down the root cause," Li says. AI can launch an initial query to aggregate results, then drill deeper based on what it finds, repeating the process iteratively.

Human engineers could follow the same routine, but it's time-consuming and difficult to execute rigorously. AI replicates the investigative process a skilled engineer would take during an incident.

For trustworthiness, Observe presents every step of the reasoning path. Engineers can modify queries and conduct their own analysis to confirm AI findings.

The Migration Question

Legacy observability vendors struggle with this shift because their rigid data models don't fit the diverse schema of raw data. They lack capabilities for advanced analytics like joins and window functions over massive datasets.

Li's advice for engineering leaders considering a migration? "Think back to a time when you were troubleshooting a tough production issue, only to realize the most relevant raw logs weren't captured because of a cost-saving decision."

Modern cloud economics have eliminated that tradeoff. The question isn't whether you can afford to store raw telemetry data. It's whether you can afford not to.

AI capabilities outrank cloud compatibility as the top criterion observability platforms.

Wed, 08 Oct 2025 11:29:12 +0000

AI-Powered Observability: What Developers Need to Know in 2025

The relationship between observability and AI is evolving rapidly, according to Dynatrace's State of Observability 2025 report. For developers and engineers, this shift represents both opportunity and challenge as observability transforms from a reactive operations tool into a proactive development necessity.

AI Becomes the Top Priority

For the first time in observability survey history, AI capabilities (29%) now rank as the most important factor when selecting observability platforms—surpassing traditional criteria like cloud compatibility (27%) and ease of data collection (21%). This dramatic shift reflects how developers increasingly rely on AI to manage the complexity of modern cloud-native architectures.

While 100% of surveyed organizations have adopted some form of AI, adoption remains concentrated in specific use cases. Data management leads at 57%, followed by AI governance (50%) and security operations (46%). Only 32% currently use AI in their observability programs, but this represents a significant growth area as teams recognize observability's role in making AI systems explainable and reliable.

The Investment Surge

Organizations are backing their AI and observability priorities with real budget increases. Seventy percent of respondents report observability budget increases in the past year, and 75% expect further increases next year. This investment reflects observability's expanding role beyond traditional IT operations into security, compliance, sustainability, and business analytics.

Current observability use cases span application performance optimization (59%), cloud application monitoring (57%), and security incident management (57%). However, the expected ROI from AI-powered observability focuses on optimizing model configurations (46%), automating remediation (41%), and detecting anomalies in model outputs (39%).

Trust and Transparency Challenges

Despite widespread AI adoption, trust concerns persist. Cybersecurity (41%) and data privacy (36%) top the list of AI usage concerns, followed by data quality (33%) and integration difficulties (29%). These concerns drive a cautious approach: humans verify 69% of AI-powered decisions, and 99% of AI governance leaders report taking human-monitored measures to validate AI decisions.

This verification requirement creates opportunities for developers. Observability platforms that provide clear explanations of AI decision-making processes become essential tools. Seventy percent of AI governance leaders report budget increases for AI trust and transparency initiatives, signaling sustained investment in making AI systems auditable and explainable.

Security Convergence

The convergence of observability and security creates new responsibilities for development teams. Ninety-eight percent of security leaders use AI to manage security compliance in some capacity, with 69% seeing increased budgets for AI-powered threat detection.

For developers, this means security considerations must be built into applications from the start. By 2030, organizations expect 50% adoption of AI-powered data encryption, risk assessments, and threat detection capabilities. AI-powered threat detection and automated patching are expected to double adoption rates within five years.

DevOps Automation Evolution

Real-time DevOps automation is expanding rapidly, with up to 50% of organizations currently using automation across various use cases. Mitigating security risks (49%) and real-time anomaly detection (50%) lead current adoption, with expectations to reach 70% within five years.

This automation trend introduces agentic AI—systems that can autonomously plan and execute tasks within guardrails. For developers, this means observability becomes crucial for ensuring safe agent behavior, monitoring model performance, and maintaining system health. The survey identifies detecting and mitigating security risks as the automation use case with the highest expected ROI (41%).

Sustainability and Efficiency

Environmental sustainability emerges as another key focus where observability proves essential. Seventy percent of organizations pursuing sustainability initiatives use observability platforms to monitor energy consumption, track emissions, and manage ESG-aligned operations in real time.

For developers, this creates opportunities to optimize code for energy efficiency. Energy-efficient software development is expected to grow from 28% adoption today to 51% by 2030. Observability platforms that translate utilization metrics (CPU, memory, network I/O) into energy usage and carbon emissions help developers understand the environmental impact of their code.

Business Observability

Perhaps the most significant shift is observability's evolution from IT tool to business capability. Only 28% currently use AI to align observability data with business KPIs, but 29% cite AI capabilities as the most important factor in observability solutions. This gap represents both a challenge and opportunity.

Developers who can connect their work to business outcomes gain strategic value. Observability platforms increasingly enable this connection by providing real-time insights into how technology impacts customer experience, operational performance, and financial efficiency.

What This Means for Developers

The convergence of AI and observability creates several imperatives for developers:

Build observability in from the start: With AI capabilities now the top platform selection criterion, teams that treat observability as an afterthought will fall behind.
Focus on explainability: As humans verify 69% of AI decisions, code that provides clear reasoning and audit trails becomes essential.
Embrace automation with guardrails: Real-time automation is expanding, but developers must ensure safe, monitored behavior.
Connect to business outcomes: The shift toward business observability means developers need to understand how their code impacts KPIs.

The State of Observability 2025 reveals that AI and observability are no longer separate concerns but converging capabilities that enable modern software development. Organizations that successfully unify these practices will lead the next wave of enterprise innovation.

Kubernetes Predictions Were Wrong

Tue, 08 Jul 2025 08:56:45 +0000

Why has the solution to the Kubernetes complexity problem proven so elusive?

In 2020, people were predicting that Kubernetes would disappear within a year. They believed someone would create a service that would reduce the adjacent choices and make Kubernetes the easy default. Everyone would use Kubernetes, but few people would work at the nuts-and-bolts level.

I checked this morning, and it’s still here — and so are the nuts and bolts. So, why has the solution to the Kubernetes complexity problem proven so elusive?

An Ecosystem of Choices

In isolation, Kubernetes isn’t particularly complicated, but a broad ecosystem of tools, options and decisions surrounds it. You can run Kubernetes anywhere, from a local machine to the cloud. You can run on a single low-powered machine or thousands of hyperscale nodes. You can use it to run one monolith or thousands of microservices.

There isn’t one opinionated way to use Kubernetes. It’s designed to be highly flexible. That means you get to make all the decisions. You choose exactly how to configure Kubernetes and which tools to use alongside it.

It’s the sheer volume of decisions you have to make to get started that people are talking about when they say things are hard, not the complexity of Kubernetes itself.

And this is before you consider the software itself. If you’ve just written a microservice in Go that runs in a container, it’s pretty easy to run it in Kubernetes. If you have a heritage monolith written under the assumption it would run on a virtual machine, there’s a great deal of work to bend it to work in a container.

It’s no wonder a developer responsible for delivering software that solves business problems reaches overload when faced with so many options along the way, especially when seen in aggregate across the different choices to be made for containers, infrastructure automation and container registries, with the various file formats and conventions used.

If you want to cycle to work, selecting a bicycle is a research-heavy task, so imagine buying a frame, wheels, handlebars, brakes and gearing separately. Some riders will want this level of control, but it’s overwhelming when your job is just getting to a destination.

Cognitive Overload and Platform Teams

The view that Kubernetes would settle into quiet utility and effectively disappear while also running all our workloads failed to materialize. Nobody managed to create a single opinionated path for Kubernetes that would take care of all these choices.

The simple reason for this is that the mythical one true way wouldn’t work for most applications and services. It’s impossible to create a single simple path without acknowledging the context of the application and organization.

This is why platform engineering has gained traction. While there’s little chance of creating an industrywide path of simplified choices, creating one within an organization is perfectly feasible.

A minimal viable platform could be a wiki page listing pre-baked decisions and providing a standard example for each configuration file. This might evolve into a facade that allows developers to specify what they need along a simple dimension, such as “size,” with the platform taking care of the details behind the flag.

Platforms should provide simplified ways to do the right thing while letting expert developers peel back the layers when the standard approach isn’t suitable.

Fewer Options Without Limiting Teams

The lack of a single opinionated path for Kubernetes underscores the importance of contextual factors for the application and organization. While creating a universal, simplified approach is impractical, platform engineering is one way to reduce the many decisions that need to be made along the way, especially when such decisions aren’t crucial.

Despite the perceived complexity issues, Kubernetes has done nothing but grow. Clearly, the benefits outweigh the adoption pain. So, imagine what would happen if Kubernetes and the associated moving parts were easier to adopt?

Platform teams could be the crucial factor in creating an opinionated Kubernetes setup that still respects the local context. Perhaps the predictions aren’t wrong, just delayed.

Portworx eliminates K8s storage complexity with AI-powered automation and declarative configs.

Tue, 24 Jun 2025 15:42:37 +0000

How Portworx and Pure Storage Are Eliminating Developer Storage Headaches

The cloud-native revolution has transformed how we build and deploy applications, but one challenge has persisted: managing persistent storage in dynamic, containerized environments. At Pure Storage's recent Accelerate conference, Venkat Ramakrishnan, VP and GM of Portworx, painted a compelling picture of how Pure's Enterprise Data Cloud vision is finally solving this problem for developers, engineers, and architects.

The Core Problem: Too Much Complexity, Too Few People

"The biggest problem we have in the industry today is that enterprise businesses and the way they're managing their infrastructure and application infrastructure is outstripping the ability to hire skilled people," Ramakrishnan explained during our interview. This skills gap has created a critical bottleneck where development teams are spending more time wrestling with infrastructure than building innovative applications.

The traditional approach to storage in Kubernetes environments has been fragmented and complex. Developers often find themselves filing tickets and waiting months for storage provisioning, or worse, trying to manage storage configurations themselves without the proper expertise. This friction slows down development cycles and prevents teams from achieving the agility that containerization promises.

Portworx: Built for Cloud-Native from Day One

Unlike legacy storage solutions that were retrofitted for containers, Portworx was designed specifically for the ephemeral, distributed nature of Kubernetes workloads. "Portworx has been working in a high automation manner in Kubernetes," Ramakrishnan noted, "and with Pure's vision, Portworx thrives even more."

The key differentiator lies in Portworx's deep integration with Kubernetes itself. While traditional storage solutions rely on external drivers and connectors, Portworx operates both its control plane and data plane within the Kubernetes cluster. This means it's inherently aware of application deployments, pod scheduling, and scaling decisions.

"We are running with API-level integrations," Ramakrishnan explained. "We are very much aware of how an application is being deployed, where each of those application containers are being deployed, and we have built a global distributed control plane and distributed storage plane."

Declarative Simplicity: Storage That Just Works

For developers familiar with Kubernetes' declarative approach, Portworx feels natural. Instead of complex storage configurations, teams can define their requirements using familiar Kubernetes constructs like storage classes. Need a database preset? Simply specify your IOPS requirements, bandwidth needs, recovery objectives, and disaster recovery characteristics—all through declarative specifications.

"A typical containerized deployment will look very simply like a declarative spec that you apply," Ramakrishnan demonstrated. "Here's my SQL database, here's the availability capability that I need, here's how I want the database to be protected, and then you just hit apply."

This approach eliminates the traditional six-month wait times for storage provisioning that many enterprise developers have experienced. The entire process becomes as simple as deploying any other Kubernetes resource.

AI-Powered Intelligence: Your Storage Copilot

Pure Storage's integration of AI through their Copilot technology brings unprecedented intelligence to storage management. The system leverages 13 years of telemetry data to make intelligent placement decisions automatically.

For Kubernetes administrators, this translates to powerful natural language queries that would previously require deep technical knowledge and manual investigation:

"What is the availability of my applications?"
"Show me where my applications are located and their topology"
"How much storage is each of my namespaces using?"
"Show me all the security vulnerabilities or certifications that need
attention"

This AI-driven approach means developers can focus on building applications rather than becoming storage experts. The system handles optimization, placement, and maintenance automatically while providing clear insights when human intervention is needed.

Unified Management: One API for Everything

Perhaps most exciting for platform engineering teams is Pure's unified API strategy. Through a single interface, teams can now manage both traditional VM-based workloads and modern containerized applications. This consolidation eliminates the need to learn and maintain multiple storage management systems.

"You can manage VMs and containers and the data associated with it through one single API," Ramakrishnan confirmed. "There's an SDK that we have published that a lot of customers use."

Security and Compliance Made Simple

Container environments present unique compliance challenges due to their dynamic nature, but Portworx addresses this through built-in security capabilities. The platform includes end-to-end multi-tenancy with what Ramakrishnan calls "app-to-flash multi-tenancy," ensuring that applications are fully isolated and secure even when sharing cluster resources.

This security model has been battle-tested with large financial services customers and highly regulated industries, providing the enterprise-grade security that developers need without adding complexity to their workflows.

Edge Computing Ready

As Kubernetes deployments expand to edge computing environments, Portworx's distributed architecture proves invaluable. The intelligent control plane is designed to be distributed rather than centralized, making it naturally suited for edge deployments where connectivity and latency matter.

"The control plane intelligence is going to be distributed," Ramakrishnan explained. "It's completely extensible, and Kubernetes, being a distributed system, fits together perfectly with this approach."

Internal Developer Platforms: The Perfect Fit

For organizations building internal developer platforms (IDPs), Portworx represents a perfect fit. Multiple application teams can share the same cluster while maintaining proper isolation and governance. The declarative nature means developers get self-service capabilities without sacrificing enterprise controls.

"IDPs are built so developers don't have to file a ticket and wait," Ramakrishnan emphasized. "Everything is declarative, and now with Pure's unified storage behind it, developers get the data management they need without ever waiting for it."

The Future: Serverless-Ready Storage

Even as computing moves toward serverless and function-as-a-service models, Ramakrishnan sees persistent storage becoming more, not less, important. "I don't think there's any serious application without persistent data," he noted. "Serverless will actually generate more data and will require more data to process and analyze."

Bottom Line: Focus on Code, Not Storage

The integration of Portworx with Pure's Enterprise Data Cloud represents a fundamental shift from managing storage to managing data through intelligent automation. For developers, engineers, and architects, this means:

Faster deployment cycles with automated storage provisioning
Simplified operations through declarative configurations
Enhanced security with built-in multi-tenancy and compliance
Intelligent optimization powered by AI and machine learning
Unified management across hybrid and multi-cloud environments

As Ramakrishnan put it, "We are in the business of accelerating applications. Everything we're doing is helping the world move faster." For development teams tired of storage complexity holding back their innovation, that acceleration can't come soon enough.

The message is clear: storage should be an invisible infrastructure that enables great applications, not a barrier to building them. With Portworx and Pure Storage's Enterprise Data Cloud, that vision is finally becoming reality.

Pure Accelerate 2025: Enterprise Data Cloud, FlashArray XL R5, AI Copilot GA for developers.

Sat, 21 Jun 2025 21:09:24 +0000

Pure Accelerate 2025: How the Enterprise Data Cloud is Revolutionizing Storage for Developers

Pure Storage's annual Accelerate conference delivered a treasure trove of announcements that will fundamentally change how developers, engineers, and architects approach data storage and management. From the introduction of the Enterprise Data Cloud concept to powerful new hardware and AI-driven automation tools, this year's event showcased solutions designed to make technology professionals' lives significantly simpler.

The Enterprise Data Cloud: A Paradigm Shift

The marquee announcement at Pure Accelerate 2025 was the introduction of the Enterprise Data Cloud (EDC) – a revolutionary approach that transforms how organizations think about data storage. Instead of managing individual storage arrays and dealing with data silos, the EDC enables teams to manage data across their entire estate through a unified, intelligent control plane.

As Pure Storage CTO Rob Lee explained, traditional enterprise storage has fallen behind consumer technology in terms of simplicity and automation. While consumers enjoy seamless, cloud-like data management across devices, enterprise teams still struggle with fragmented systems, manual workflows, and data blindness.

The EDC addresses three critical transformations:

Storage Automation: Managing thousands of arrays as a single, unified system
IT Operations Transformation: Shifting from manual steps to declarative workflows
Data Set Management: Gaining complete visibility into data location, usage, and ownership

Pure Fusion: The Foundation for Unified Management

At the heart of the EDC is Pure Fusion, now enhanced with workload automation capabilities. This distributed intelligent control plane allows developers to:

Provision resources declaratively: Specify performance and capacity requirements independently, letting the system determine optimal hardware allocation
Manage fleets programmatically: Every Pure Storage array becomes discoverable and manageable through consistent APIs
Implement policy-driven workflows: Set business intent and let automation handle the implementation details

The new workload automation features include protocol-agnostic provisioning across file, block, and object storage, plus preset configurations for common database and application scenarios. This means developers can focus on application logic rather than storage infrastructure minutiae.

Next-Generation Hardware for Extreme Performance

Pure Storage unveiled three significant hardware advances designed to handle the most demanding modern workloads:

FlashArray//XL R5: Performance Density Champion

The FlashArray//XL R5 delivers unprecedented performance density, doubling IOPS per rack unit compared to the previous generation while increasing maximum raw capacity by 50%. Key specifications include:

Up to 1.9PB raw capacity (7.4PB effective with data reduction)
Support for up to 200% more database transactions than competitive solutions
Capability to run up to 300 more VMs per TB than alternative systems

For developers working with large OLTP databases, virtualized environments, or in-memory applications, this translates to significant consolidation opportunities and reduced infrastructure complexity.

FlashArray//ST: Ultra-Low Latency Specialist

The new FlashArray//ST targets the most latency-sensitive workloads, delivering over 10 million IOPS per five rack units through an optimized I/O path. This makes it ideal for:

In-memory databases
High-frequency trading applications
Real-time analytics platforms
Scale-out NoSQL databases requiring sub-microsecond response times

FlashBlade//S R2: Scale-Out Performance Leader

The FlashBlade//S R2 provides up to 30% better performance than competitors across critical workloads like genome sequencing, AI inference, and electronic design automation. This enhanced platform accelerates time-to-insight for data-intensive applications and supports the massive datasets required for effective AI reasoning.

Unified Protocol Support: Simplifying Architecture

A significant development for developers is Pure Storage's extension of object support to FlashArray, creating a single architecture supporting block, file, and object protocols. This unified approach:

Reduces architectural complexity: Eliminates the need for separate storage systems for different data types
Simplifies data management: Consistent experience across all storage protocols
Enables flexible application design: Developers can choose optimal data access patterns without infrastructure constraints

AI Copilot Goes General Availability

The AI Copilot has moved from beta to general availability, providing developers and administrators with an always-on assistant that delivers personalized, fleet-aware insights. Key capabilities include:

Natural language queries: Ask "Find my performance bottlenecks" or "Show me security vulnerabilities"
Proactive recommendations: Automatic identification of optimization opportunities
Knowledge base integration: Instant access to CLI guides, API documentation, and troubleshooting resources

This tool significantly reduces the learning curve for storage management and accelerates problem resolution, allowing teams to focus on higher-value activities.

Enhanced Security and Automation Workflows

Rubrik Security Cloud Integration

The integration with Rubrik Security Cloud represents a breakthrough in automated cyber recovery. When threats are detected:

Pure Fusion automatically tags indelible SafeMode snapshots
Rubrik's ransomware scanning identifies clean data for fast restore
Near-zero RTO recovery minimizes business disruption

This integration operates through Pure1 Workflow Automation, reducing manual effort and improving compliance posture.

CrowdStrike LogScale Partnership

The validated integration with CrowdStrike's Falcon LogScale provides organizations with:

Optimized on-premises storage for log analytics
Accelerated threat detection and investigation
Scalable, high-performance architecture for security operations

Workflow Orchestration: Full-Stack Automation

The new Workflow Orchestration capability extends automation beyond storage to encompass the entire IT stack. Built on thousands of existing connectors to applications like Cisco, Microsoft, VMware, ServiceNow, and Slack, this feature enables:

Pre-built application recipes: Standardized deployment patterns for common scenarios
Custom workflow creation: Tailored automation specific to organizational needs
End-to-end orchestration: Coordination across storage, compute, network, and application layers

For DevOps teams, this represents a significant step toward infrastructure-as-code maturity and reduces deployment complexity.

Service-Oriented Outcomes: Beyond Generic Tiers

Pure Storage introduced innovative service models that align with business outcomes rather than traditional storage metrics:

Evergreen//One Adaptive Tier

This new service tier allows organizations to:

Set performance requirements independently of capacity
Scale resources dynamically without hardware migration
Pay for actual usage patterns rather than predetermined configurations

Medical Imaging as a Service

In a industry-first approach, Pure Storage announced a medical imaging service billed per imaging study (X-ray, CT scan, MRI) rather than traditional storage metrics. This outcome-based model includes:

Fixed costs for 10-year data retention
Automated data lifecycle management
Compliance with healthcare regulations

Snapshot Packages

The new snapshot add-on service eliminates sizing complexity by providing:

Predictable, policy-based pricing for snapshot usage
One year of included snapshot retention
Transparent billing without capacity surprises

Direct Flash Module Evolution

Pure Storage continues pushing storage density boundaries with 300TB Direct Flash Modules (DFMs) – doubling the density of the largest QLC DFMs in the market. This advancement:

Increases energy efficiency per TB stored
Reduces datacenter footprint requirements
Maintains Pure Storage's independence from commodity SSD market limitations

Implications for Developers and Engineers

These announcements collectively address several pain points that have long plagued development teams:

Simplified Infrastructure Management: Declarative APIs and policy-driven automation reduce operational overhead
Performance Predictability: SLA-driven services ensure consistent application performance
Enhanced Security Posture: Integrated threat detection and automated recovery minimize security risks
Reduced Time-to-Market: Workflow orchestration accelerates deployment cycles
Cost Optimization: Usage-based pricing models align costs with business value

The Energy Efficiency Imperative

CTO International Alex McMullan highlighted the critical challenge of energy consumption in modern datacenters. With AI workloads consuming exponentially more power, Pure Storage's approach of delivering dramatically increased capacity and performance within the same power envelope becomes crucial for sustainable operations.

The company's 1,200x improvement in storage capability over the past decade – from 5TB to 6PB systems – while reducing power requirements demonstrates the potential for technological innovation to address environmental challenges.

Looking Forward

Pure Accelerate 2025 positioned the Enterprise Data Cloud not just as a technological evolution, but as a fundamental reimagining of how organizations approach data management. For developers, engineers, and architects, these innovations promise to:

Abstract infrastructure complexity through intelligent automation
Provide cloud-like simplicity in on-premises environments
Enable focus on application value rather than storage administration
Deliver predictable performance and costs at any scale

The shift from managing storage to managing data represents more than a semantic change – it's a paradigm shift that could free technical teams to focus on innovation rather than infrastructure maintenance.

As AI continues driving exponential data growth, the tools and approaches announced at Pure Accelerate 2025 provide a roadmap for managing this complexity while maintaining the agility and simplicity that modern development practices demand.

How Portworx eliminates K8s storage complexity with AI-powered automation and declarative configs.

Sat, 21 Jun 2025 05:41:25 +0000

How Portworx and Pure Storage Are Eliminating Developer Storage Headaches

The Core Problem: Too Much Complexity, Too Few People

Portworx: Built for Cloud-Native from Day One

Declarative Simplicity: Storage That Just Works

AI-Powered Intelligence: Your Storage Copilot

For Kubernetes administrators, this translates to powerful natural language queries that would previously require deep technical knowledge and manual investigation:

"What is the availability of my applications?"
"Show me where my applications are located and their topology"
"How much storage is each of my namespaces using?"
"Show me all the security vulnerabilities or certifications that need
attention"

Unified Management: One API for Everything

"You can manage VMs and containers and the data associated with it through one single API," Ramakrishnan confirmed. "There's an SDK that we have published that a lot of customers use."

Security and Compliance Made Simple

Edge Computing Ready

Internal Developer Platforms: The Perfect Fit

The Future: Serverless-Ready Storage

Bottom Line: Focus on Code, Not Storage

Faster deployment cycles with automated storage provisioning
Simplified operations through declarative configurations
Enhanced security with built-in multi-tenancy and compliance
Intelligent optimization powered by AI and machine learning
Unified management across hybrid and multi-cloud environments

DDN unveils Infinia 2.0: Sub-millisecond data access for AI development at scale.

Sun, 15 Jun 2025 11:56:23 +0000

DDN Unveils Infinia 2.0: Sub-Millisecond Data Access for AI Development at Scale

Data intelligence company DDN has emerged from relative obscurity with a compelling story about solving one of AI development's most persistent challenges: getting data to GPUs fast enough to maximize training efficiency. At the recent IT Press Tour, DDN demonstrated how their new Infinia 2.0 platform is reshaping the infrastructure stack for AI applications, particularly for developers building at scale.

The GPU Utilization Problem

DDN's value proposition centers on a critical bottleneck many machine learning engineers face: GPU idle time during model training. As CEO Paul Bloch explained, "When people buy 20,000 GPUs, that's a $3 billion investment. The last thing you want is idle GPUs." DDN's data shows that without optimized storage, GPUs can sit waiting for data up to 60% of the time during training workflows.

The company's solution involves positioning its data intelligence platform between applications and infrastructure to eliminate these bottlenecks. According to DDN, their platform increases GPU efficiency by 30% while reducing data center footprint and power consumption by 10x.

Infinia's Technical Architecture

The technical innovation behind Infinia 2.0 represents a fundamental departure from traditional storage architectures. CTO Sven Oehme detailed how the platform uses a native key-value store at its foundation, with all protocols—S3, GCS, POSIX, SQL—operating as peers rather than layers.

"Traditional storage systems have a block layer, then a file system layer, then an object layer on top," Oehme explained. "In Infinia, all data services are peers accessing the same key-value store directly. This eliminates multiple software layers and dramatically reduces latency."

The architecture enables some impressive performance claims: DDN demonstrates 100x faster object listing compared to AWS S3, 25x lower time-to-first-byte latency, and the ability to handle millions of concurrent operations. In production at Elon Musk's xAI facility, Infinia manages nearly 600 petabytes with over one million concurrent jobs.

Developer Integration Points

For developers, DDN provides multiple integration pathways. The platform offers native S3 compatibility, while also adding support for Google Cloud Storage APIs, making DDN potentially the only on-premises solution that can natively run Google Cloud applications without requiring data transformation.

The company is releasing a comprehensive SDK with bindings for multiple languages, including Python, Java, Go, and Rust. This allows developers to build custom data services directly on top of Infinia's key-value store, leveraging its high-performance characteristics.

Particularly relevant for AI development workflows, DDN supports NVIDIA's KV cache offloading, allowing GPU state to be quickly stored and restored. This enables more efficient resource utilization for inference workloads, allowing users to pause and resume sessions.

Multi-Cloud Strategy

DDN's partnership with Google Cloud represents a significant shift for the traditionally HPC-focused company. The Google Cloud Managed Lustre service, powered by DDN's EXAScaler technology, provides developers with persistent parallel file systems that can scale from small experiments to massive training runs.

"It's not marketplace—this is Google's first-party offering," Bloch emphasized. "Every Google rep is incentivized to sell this." The integration enables developers to seamlessly move workloads between on-premises and cloud environments, using the same storage APIs and performance characteristics.

Real-World Performance Gains

DDN's most compelling demonstration involved a retrieval-augmented generation (RAG) pipeline running on AWS infrastructure. By simply replacing AWS S3 with Infinia software running on AWS virtual machines, they achieved 22x performance improvement while reducing costs by over 60%.

The demo used unmodified AWS microservices, changing only the storage layer. This suggests developers could potentially achieve significant performance gains without rewriting applications—a crucial consideration for teams with existing AI infrastructure investments.

Industry Context

DDN's emergence coincides with the rapid evolution of the AI infrastructure market. The company reports powering over 700,000 GPUs across customer deployments, with individual clusters reaching 200,000 GPUs. Their recent $300 million investment from Blackstone at a $5 billion valuation reflects growing recognition that data infrastructure will be critical as AI moves from training to inference at scale.

"We're seeing customers asking for multiple exabytes in single deployments," Oehme noted. "The data requirements are growing faster than compute in many cases."

Looking Forward

For developers building AI applications, DDN's technology suggests a future where data infrastructure becomes increasingly software-defined and protocol-agnostic. The ability to run the same storage platform across edge devices, exascale supercomputers, and maintain consistent APIs across cloud and on-premises environments could significantly simplify the development and deployment of distributed AI systems.

As AI applications transition from foundational models to production inference workloads serving millions of users, the emphasis on ultra-low latency data access that DDN provides may become a table-stakes requirement rather than a competitive advantage. For now, their technology appears to be pushing the boundaries of what's possible in high-performance data infrastructure.

Discover how Panzura's cloud-native file system eliminates collaboration headaches for developers in distributed teams.

Sat, 10 May 2025 23:54:27 +0000

How Panzura Makes Developers' Lives Easier in Multi-Cloud Environments

In today's distributed development world, collaborating on projects across multiple locations can be a nightmare of file conflicts, version control issues, and performance bottlenecks. Panzura, a leader in cloud-native file services, has developed solutions that directly address these pain points for developers, engineers, and architects working in complex multi-cloud environments.

The Developer's File Collaboration Problem

If you've ever worked on a large-scale project with team members spread across different locations, you've likely encountered the "file collaboration problem." Traditional network-attached storage (NAS) systems weren't designed for global teams, and cloud storage often introduces latency, file locking issues, and versioning conflicts that can destroy productivity.

These challenges become even more pronounced when working with large binary files, ML models, or resource-intensive development environments that don't work well with traditional version control systems like Git.

How Panzura Solves Developer Headaches

Panzura's CloudFS platform transforms how distributed development teams work together by solving several critical issues that plague modern software development workflows.

1. Real-Time File Collaboration Without Conflicts

Panzura's most powerful feature for developers is its distributed file locking and byte-range locking capabilities. This enables multiple team members to work on different parts of the same file simultaneously without conflicts.

For example, engineers working on CAD designs, large datasets, or complex simulations can collaborate in real-time without the "last save wins" problem that often results in lost work. The system implements file locking at a granular level, similar to how Google Docs allows multiple editors, but for enterprise file systems and specialized development files.

2. Eliminating the "Pre-Warming Cache" Problem

In traditional distributed systems, teams often must "pre-warm" caches to ensure everyone has access to the files they need. This means administrators must predict which files teams will need and prepare them in advance.

With Panzura's CloudFS 8.5 Adapt's new Regional Store feature, this problem disappears. The platform automatically synchronizes data across cloud regions, ensuring that files are always available with minimal latency, regardless of where team members are located. This is especially valuable for development teams working with large datasets for AI/ML projects or media files, where access speeds directly impact productivity.

3. Immutability and Versioning Built Into the File System

For developers, accidental deletions or corruptions can be catastrophic. Panzura implements immutability at the file system level, meaning data is never overwritten. Instead, the system creates pointers to new blocks, preserving previous versions automatically.

This built-in versioning provides an extra layer of protection beyond Git or other VCS systems, especially for binary files and large assets. Developers can roll back to previous file versions with just a few clicks, without lengthy restores from backups.

4. Accelerating Build and Test Pipelines

With development pipelines increasingly distributed across environments, build and test processes can be slowed by file access latency. Panzura's architecture speeds up these workflows by:

Caching active files locally for fast access
Deduplicating data to reduce transfer volumes
Providing consistent, immediate file access across all locations
Enabling high-performance data access for CI/CD pipelines

This acceleration is particularly valuable for organizations running complex microservices architectures or containerized applications where components are developed and tested across multiple environments.

5. Seamless Cloud Development Environment Integration

Modern development increasingly involves shifting workloads between on-premises and cloud environments. Panzura's platform creates a consistent file system experience regardless of where computation happens.

For instance, a developer might work locally but need to run intensive computations in AWS, Azure, or GCP. With Panzura, the file system layer remains consistent across these environments, eliminating the need for complex file synchronization or transfer processes.

Real-World Example: How Regional Store Changes the Game

The upcoming CloudFS 8.5 Adapt release (February 2025) includes a powerful feature called Regional Store that dramatically improves performance for geographically distributed teams.

Consider a development team with members in San Francisco, London, and Singapore. Without Regional Store, engineers in Singapore might face significant latency when accessing files stored in a US-based object store. With Regional Store, organizations can configure up to four synchronized object storage buckets across their preferred cloud provider's regions.

This means every developer, regardless of location, experiences local-like performance when accessing uncached files. The buckets are synchronized by the cloud provider, ensuring data consistency while eliminating performance penalties for remote team members.

Security That Doesn't Get in Developers' Way

Security requirements often create friction for developers. Panzura's approach minimizes this tension by:

Implementing FIPS 140-3 certified encryption transparently
Providing role-based access control with Okta SSO integration
Including ransomware detection and interdiction that doesn't disrupt legitimate work
Allowing secure remote access without cumbersome VPNs

These security capabilities are particularly valuable for teams working on sensitive projects or in regulated industries where compliance is non-negotiable.

The Future of Development Infrastructure

As development continues to evolve toward more distributed, multi-cloud models, infrastructure that can provide seamless data access, collaboration, and security will become increasingly essential.

Panzura's vision for "autonomic" infrastructure—systems that self-heal, self-optimize, and self-protect—aligns with the needs of modern development teams. For architects and engineering leaders planning their infrastructure strategy, platforms like Panzura represent a pathway to development environments that enhance productivity rather than hindering it.

By tackling the fundamental challenges of file collaboration in distributed environments, Panzura helps development teams focus on creating great software rather than wrestling with infrastructure limitations.

Learn how Hammerspace's Global Data Platform eliminates GPU bottlenecks through unified storage for AI workloads.

Wed, 07 May 2025 16:03:44 +0000

Solving GPU Data Bottlenecks: A Developer's Guide to Hammerspace's Global Data Platform

As AI model sizes grow exponentially and GPU clusters continue to scale, many engineering teams face a critical challenge: how to efficiently deliver massive datasets to compute resources without creating bottlenecks. This challenge becomes even more complex when working across multiple environments, from on-premises to cloud infrastructure. Let's explore how Hammerspace's Global Data Platform is addressing these technical challenges and what it means for developers and architects building AI infrastructure.

The Technical Problem: Stranded Storage and Data Silos

Modern GPU servers like NVIDIA's DGX systems come with substantial local NVMe storage - often between 60-500TB per server. According to Hammerspace's research, much of this storage sits unused, creating a "stranded capacity" problem.

Why does this happen? The answer lies in how data is traditionally managed:

Local storage is typically siloed, accessible only to the local GPU server
Moving data between servers creates multiple copies and management overhead
External storage systems add cost, latency, and power consumption
Multi-site and hybrid cloud deployments create fragmented data landscapes

# Example of traditional data movement to GPU servers
for server in gpu_cluster:
    # Copy data from central storage to each server
    scp -r /datasets/training_data user@{server}:/local/storage/
    # Run training on just local data
    ssh user@{server} "python train.py --data /local/storage/training_data"

This approach is inefficient, creates data inconsistency, and wastes expensive storage resources.

Tier 0 Storage: Technical Architecture and Implementation

In late 2024, Hammerspace introduced "Tier 0" - a breakthrough approach that transforms the local NVMe storage in GPU servers into a unified, shared storage pool. Here's how it works from a technical perspective:

Architecture Components

Global Namespace Layer: Creates a single unified view of all data
Parallel File System: Enables high-performance data access across servers
Data Orchestration Engine: Intelligently places and moves data based on access patterns
Linux-Native Implementation: Works with standard file systems without proprietary clients

Deployment Model

The platform can be deployed in under 30 minutes on bare metal, VMs, or in the cloud. It's designed to integrate with existing infrastructure without disrupting workflows.

# Example deployment architecture
- Hammerspace Data Services (installed on each GPU server)
  |- Direct integration with local NVMe
  |- Parallel file system interface
  |- Global metadata service
- Unified namespace mount point (/global)
  |- Appears as a standard file system
  |- Accessible from all compute resources

Performance Validation: MLPerf Benchmarks

Note: Performance is critical for AI/ML workloads - any bottlenecks in data access directly impact model training times and GPU utilization.

MLPerf benchmark results demonstrate the performance advantages of this approach:

Hammerspace Tier 0 Internal Storage achieved 33 accelerators with a single client
This represents a 32% improvement over traditional external storage (25 accelerators)
Extrapolated results with 18 clients showed support for up to 594 accelerators

Real-World Implementation Example: Meta's LLM Training

One of the most impressive technical implementations comes from Meta, which deployed Hammerspace for Llama large language model training. According to Meta's engineering team:

"Hammerspace enables engineers to perform interactive debugging for jobs using thousands of GPUs as code changes are immediately accessible to all nodes within the environment."

For developers, this means:

Code changes propagate instantly across the environment
No need for complex synchronization mechanisms
Simplified debugging of distributed training jobs
Maximized GPU utilization across large clusters

Technical Implementation Guide

Step 1: Architecture Planning

When implementing a global data platform for AI workloads, consider this tiered approach:

Tier 0 (Microseconds) - Local NVMe
- Best for: Active training datasets, checkpoints
- Latency: ~100-200 microseconds

Tier 1 (Milliseconds) - Shared NVMe
- Best for: Preparatory datasets, intermediate results
- Latency: ~1-2 milliseconds

Tier 2 (Seconds) - Standard Storage
- Best for: Dataset archives, completed models
- Latency: ~5-10 seconds

Archive (Minutes)
- Best for: Long-term retention
- Policy-based movement

Step 2: Integration Points

The platform provides several integration options:

Standard Interfaces:
- POSIX-compliant file system
- Native Linux access patterns
- Support for NFS, SMB, and S3 protocols
Orchestration Integration:
- Kubernetes integration via CSI
- API-driven management
- Infrastructure-as-Code support

Step 3: Implementation Considerations

For successful implementation, consider these technical factors:

Network Architecture:
- Design for maximum storage network bandwidth
- Consider RDMA networks for ultra-low latency
- Plan interconnects between GPU nodes
Data Access Patterns:
- Analyze your workload's read/write patterns
- Identify hot vs. cold data
- Configure appropriate data placement policies
Monitoring Setup:
- Implement metrics collection for storage performance
- Monitor GPU utilization to identify bottlenecks
- Track data movement between tiers

Expanding Ecosystem and Integration Options

Hammerspace has continued to expand its ecosystem with strategic partnerships:

Hitachi Vantara Integration: Recently announced integration with VSP One storage platform, providing a full-stack AI infrastructure solution.
Yition.ai Partnership: Collaboration to bring hyperscale AI storage solutions to the Chinese market, focusing on large-scale multimodal datasets.

Both partnerships focus on allowing engineering teams to build GPU infrastructures that can effectively handle the massive unstructured data needs of modern AI workloads.

Technical Advantages for Developers and Architects

From a developer's perspective, this architecture delivers several key benefits:

Simplified Data Access:
- Single mount point for all data
- No need to manage data copying between systems
- Consistent view across development and production
Accelerated Development Cycles:
- Faster data preparation and cleaning pipelines
- Improved checkpointing for training resilience
- Reduced time from development to production
Resource Optimization:
- Better utilization of expensive GPU resources
- Reduced storage duplication
- Lower power consumption through efficient resource use

Conclusion

The challenges of managing data for AI workloads continue to grow in complexity as models and datasets increase in size. Hammerspace's approach to unifying data access across infrastructure represents a significant technical advancement for engineering teams building AI systems.

By transforming stranded local storage into a unified, high-performance resource, the platform eliminates traditional data bottlenecks that have plagued GPU infrastructure. For developers and architects, this means being able to focus more on building and optimizing models rather than managing complex data movement and storage systems.

The platform's ability to span from microsecond-latency Tier 0 storage to archival solutions within a single namespace gives engineering teams the flexibility to implement appropriate data lifecycle management while maintaining consistent access patterns.

As the AI infrastructure landscape continues to evolve, unified data access and intelligent orchestration will become increasingly critical components of successful implementations.

FAQ

How does this differ from traditional distributed file systems?

Traditional distributed file systems typically rely on dedicated storage servers, while Hammerspace's approach can leverage the existing NVMe storage in GPU servers, creating a more efficient and lower-latency solution without additional specialized hardware.

What's the impact on existing workflows and code?

The platform appears as a standard file system to applications, meaning existing code doesn't need to be modified to take advantage of the unified namespace and performance benefits.

How does this approach handle data consistency across locations?

The global metadata service maintains consistency across all locations, with configurable consistency models depending on workload requirements. This ensures that all compute resources see a consistent view of the data without manual synchronization.