Dynatrace extended its advanced observability capabilities to cloud and data center environments running on the Linux ARM platform.
These enhancements to Dynatrace reflect increasing demand from enterprise IT teams for containers and microservices, as well as their growing adoption of ARM-based servers as an efficient and cost-effective platform for cloud-native environments. To avoid wasting time and resources on manual configuration, teams need continuous automation to self-discover and automatically instrument changes in their environment, and to capture all observability data in real time.
“In modern IT environments, containers, cloud applications, and microservices can come and go in seconds. Teams can’t waste time attempting to maintain observability,” said Steve Tack, SVP of Product Management, Dynatrace. “That’s why we’re extending Dynatrace’s advanced observability and continuous automation to environments running on ARM. Unlike alternative solutions, that don’t support modern architectures or require special add-ons and manual effort to instrument and maintain, Dynatrace on ARM just works. There’s no configuration or scripting required, and no need to know which apps or cloud platforms teams are running. Customers using Dynatrace on ARM experience the fast time to value they’ve come to expect from us.”
The Dynatrace platform has a rich history of providing automatic and continuous observability for dynamic multicloud environments. Dynatrace uses this observability data to create and continuously update a complete entity map, ensuring an always-accurate view of how everything in an IT environment is interconnected, including the millions or even billions of dependencies across the full stack. As a result, the Dynatrace AI engine, Davis, does not need to learn or be trained on the environment, because the entity map details what it needs to know. Davis then helps teams by providing precise answers in real time and prioritizing what matters, which reduces noise and enables people to focus on innovating instead of problem solving.
Key enhancements to the Dynatrace platform include advanced observability for Linux running on the ARM 64-bit architecture, across infrastructure, networks, applications, containers and microservices, and including code-level visibility into application languages like Java, NGINX, and Node.js. In addition, Dynatrace enables continuous automation spanning the full stack and without manual configuration – from discovery and instrumentation, to baselining, anomaly detection, root cause analysis, and auto-remediation. With these enhancements, Dynatrace adds to its extensive coverage for server architectures, which also includes Microsoft Windows, Linux x86/x64, AIX, PPCLE, Linux on Z, and z/OS.
The Latest
I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...
Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...
For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...
Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...
Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...
For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...
New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...
Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...
In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ...
In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...