
Emulex Corporation and Dynatrace today announced NetPod, a fully integrated solution that combines Dynatrace’s Data Center (DC RUM) analysis with Emulex’s EndaceProbe Intelligent Network Recorders.
NetPod provides network teams with high-fidelity network and application transaction-level visibility and long-term packet storage. NetPod hardware-based EndaceProbe INRs provide 100 percent packet capture, nanosecond time stamping, and “back-in-time” playback capabilities, as well as a platform for user experience monitoring. NetPod enables optimization of the performance, scalability and predictability of all applications in the data center, including Citrix, SAP, Oracle, Siebel, Microsoft Exchange and many others.
“NetPod is the result of more than a year of collaboration between Emulex and Dynatrace, motivated by demand from mutual enterprise and service provider clients,” explained John Van Siclen, GM, Dynatrace. “NetPod uniquely understands application logic and user behavior for the leading applications, databases and middleware. This integrated solution closes the gap between network and application teams, giving them a unified set of insights to manage their digital channels and infrastructure.”
NetPod is a solution designed for high-speed 10/40/100Gb Ethernet networks to provide complete visibility across multiple tiers, infrastructure components and web and non-web applications. It intelligently monitors and records real-time application transactions across the most complex application delivery infrastructures spanning all leading load balancers, firewalls, servers, WAN accelerators, and middleware.
“Today’s IT teams need end-to-end tools capable of doing more than assisting in monitoring and identifying performance issues,” said Ali Hedayati, SVP and GM, Network Visibility Products, Emulex. “Whether the problem is isolated to the client, network, server or database, the underlying packet data provided by NetPod can be used to ensure that the technology team responsible is provided irrefutable evidence of the true source of the problem. The application context provided by NetPod also supports rapid extraction of the right packets at the right time, even when a transaction of interest occurred in the past. All of this simplifies the delivery of mission-critical, network-centric applications and increases the business value of these applications by ensuring their availability to end users.”
NetPod will be available in Q1 calendar 2015 from select Emulex and Dynatrace partners with the requisite expertise in AA-NPM and APM. Forsythe and WWT were chosen as launch partners in North America because of their expertise in AA-NPM and APM, and ability to support NetPod customers.
NetPod combines several critical technologies to simplify and automate application transaction analysis:
- Continuous assessment of user experience and business impact of performance and availability issues;
- Real-time packet capture (with no packet loss) and nanosecond-scale time stamping to ensure that all of the required data is available for analysis;
- Deep storage of captured packets, enabling “back-in-time” playback analysis when intermittent issues occur;
- A comprehensive set of application protocol decodes, combined with application logic and user context; and
- Seamless compatibility with Dynatrace APM technologies as well as third party products requiring access to packets for security, for deep transaction tracing at the code level, synthetic monitoring, or mobile device monitoring.
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 ...
