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ScienceLogic Debuts CloudMapper

ScienceLogic released CloudMapper, permitting organizations to monitor and visualize their off-premise, Amazon Web Services (AWS) resources.

Nearly every enterprise on earth uses AWS in some capacity. One of every three applications hosted in the world resides on AWS, it contains half-a-million Linux servers and growing, and it adds more storage capacity every 24 hours today than in all of 2005.

With CloudMapper, enterprise AWS users can simply access an IT monitoring platform on AWS that provides them full IT performance visibility across all Amazon assets.

CloudMapper benefits include:

- Unprecedented Visibility into AWS Cloud Infrastructure

Covering virtually every Amazon service offering currently available. For AWS instances, operating system and software applications, visibility is provided in an agent or agent less model.

Enterprises are primarily concerned with operating system and application health, but historically all they can see from AWS native reporting are rudimentary server infrastructure performance and availability metrics.

CloudMapper closes that critical gap. Enterprises can view AWS asset performance just like it’s in their own data center.

- Dynamic and Visual Linkage

IT operations can now, for the first time, literally ‘see’ the critical interdependencies across all application assets in and out of the cloud workloads, on one screen. With this level of in-depth visibility, enterprises can immediately remediate issues that can cripple application performance

IT operations can see where all workloads are physically running—across the cloud—and how they are connected to one another as they change, and how they depend on each other, helping to mitigate risk during a natural disaster or technical outages.

Enterprises can finally address cloud sprawl across the many AWS accounts, zones, regions, and services, making it simple to ‘see’ where resources are needed, where they need to be cut back, and immediately see the impact of changes made.

- Instant Access to Cloud Monitoring

You provide your Amazon credentials, the dashboard goes live. Why it matters? A major breakthrough in how IT monitoring functionality is purchased and provisioned.

- Simplified Benchmarking

Enterprises to date have been reluctant to move a larger share of their application assets to AWS, not only based on cost, visibility, and security, but also lack of visibility into configuration, performance, and reliability. With ScienceLogic CloudMapper in place, enterprises for the first time can readily determine risk levels and AWS investment ROI.

The Latest

Cloud outages are usually described as technical failures. When a service goes down, a dependency breaks, or a region has issues, the focus immediately shifts to infrastructure. But if you look closely at how these incidents actually unfold, the root cause is rarely the technology itself. It is almost always tied to decisions made earlier, during design, implementation, or day-to-day operations. The system behaves the way it was built. The real question is how it was built ...

77% of leaders say their teams need AI skills urgently. 64% say their organization plans to train current employees rather than hire new ones. So far, so reasonable. The part that surprised me is who's been put in charge: 34% of those leaders say IT and engineering own the AI skills mandate. Learning and Development or HR own it at 7% of organizations. That's roughly five-to-one in favor of the people who understand the tools, over the people whose actual job is teaching adults how to learn new ones ...

In the ever-evolving digital landscape, enterprises are increasingly focused on enhancing their observability stacks to gain deeper insights into their IT environments. Observability has become a cornerstone of modern IT operations, enabling organizations to monitor, diagnose, and optimize their systems with unprecedented precision. However, a critical piece of the puzzle often goes unnoticed in this transformation: IBM i ...

We just surveyed 300 frontend and mobile engineers across 16 countries, and the finding that keeps sticking with me isn't the one about AI. It's this: 74% of engineering teams rate themselves in the "middle" of the observability maturity scale. Not reactive, not strategic. Stuck in the middle. They have dashboards, they have tracing, they have alerts. And yet when something goes wrong, they still can't tell you why ...

In MEAN TIME TO INSIGHT Episode 25, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses  AI's impact on the Wide Area Network (WAN) ... 

Application performance monitoring (APM) dashboards are only as useful as what they are configured to measure. The default setup covers obvious failure modes such as downtime, error spikes, and latency breaches, but it does not cover everything. Some failures produce no alerts or anomalies. The dashboard stays green while users experience a broken product. Here are six signs that is happening ...

The race to deploy AI is largely over. Most enterprises have entered it. The question now is not whether artificial intelligence is running inside the organization. The question is whether anyone is genuinely responsible for what it does. That is not a technical question. It is a leadership one. And most organizations are not yet structured to answer it honestly ...

A new analysis of 250 real-world queries across common retail tasks, such as product pricing, availability, ratings, shipping and specifications, reveals systemic inefficiency at the heart of web-based AI agents. On average, 97.9% of the data retrieved by agents from live web pages is irrelevant to the query being answered. Specifically, the average page ingested ran nearly 9,000 characters, while the average answer was just 32 characters, resulting in a noise-to-signal ratio of 278:1. Price queries were the most extreme outlier, with noise rates approaching 99.5%. That's not a rounding error. That's a structural problem ...

The enterprises that will define the next decade are not the ones that deployed the most technology. They are the ones who understood what their technology was actually doing. That distinction is not a philosophical point. It is the central operational challenge facing every organization that has spent the last five years modernizing at speed ...

AI is becoming the operating system of the enterprise. It acts as an invisible coordination layer that understands intent, connects systems, and executes work across complex SaaS environments. Previously, employees had to click through multiple systems — CRM, ERP, support tools, collaboration platforms — to complete a single task. Now, instead of navigating each application manually, they can simply state what they need to accomplish ...

ScienceLogic Debuts CloudMapper

ScienceLogic released CloudMapper, permitting organizations to monitor and visualize their off-premise, Amazon Web Services (AWS) resources.

Nearly every enterprise on earth uses AWS in some capacity. One of every three applications hosted in the world resides on AWS, it contains half-a-million Linux servers and growing, and it adds more storage capacity every 24 hours today than in all of 2005.

With CloudMapper, enterprise AWS users can simply access an IT monitoring platform on AWS that provides them full IT performance visibility across all Amazon assets.

CloudMapper benefits include:

- Unprecedented Visibility into AWS Cloud Infrastructure

Covering virtually every Amazon service offering currently available. For AWS instances, operating system and software applications, visibility is provided in an agent or agent less model.

Enterprises are primarily concerned with operating system and application health, but historically all they can see from AWS native reporting are rudimentary server infrastructure performance and availability metrics.

CloudMapper closes that critical gap. Enterprises can view AWS asset performance just like it’s in their own data center.

- Dynamic and Visual Linkage

IT operations can now, for the first time, literally ‘see’ the critical interdependencies across all application assets in and out of the cloud workloads, on one screen. With this level of in-depth visibility, enterprises can immediately remediate issues that can cripple application performance

IT operations can see where all workloads are physically running—across the cloud—and how they are connected to one another as they change, and how they depend on each other, helping to mitigate risk during a natural disaster or technical outages.

Enterprises can finally address cloud sprawl across the many AWS accounts, zones, regions, and services, making it simple to ‘see’ where resources are needed, where they need to be cut back, and immediately see the impact of changes made.

- Instant Access to Cloud Monitoring

You provide your Amazon credentials, the dashboard goes live. Why it matters? A major breakthrough in how IT monitoring functionality is purchased and provisioned.

- Simplified Benchmarking

Enterprises to date have been reluctant to move a larger share of their application assets to AWS, not only based on cost, visibility, and security, but also lack of visibility into configuration, performance, and reliability. With ScienceLogic CloudMapper in place, enterprises for the first time can readily determine risk levels and AWS investment ROI.

The Latest

Cloud outages are usually described as technical failures. When a service goes down, a dependency breaks, or a region has issues, the focus immediately shifts to infrastructure. But if you look closely at how these incidents actually unfold, the root cause is rarely the technology itself. It is almost always tied to decisions made earlier, during design, implementation, or day-to-day operations. The system behaves the way it was built. The real question is how it was built ...

77% of leaders say their teams need AI skills urgently. 64% say their organization plans to train current employees rather than hire new ones. So far, so reasonable. The part that surprised me is who's been put in charge: 34% of those leaders say IT and engineering own the AI skills mandate. Learning and Development or HR own it at 7% of organizations. That's roughly five-to-one in favor of the people who understand the tools, over the people whose actual job is teaching adults how to learn new ones ...

In the ever-evolving digital landscape, enterprises are increasingly focused on enhancing their observability stacks to gain deeper insights into their IT environments. Observability has become a cornerstone of modern IT operations, enabling organizations to monitor, diagnose, and optimize their systems with unprecedented precision. However, a critical piece of the puzzle often goes unnoticed in this transformation: IBM i ...

We just surveyed 300 frontend and mobile engineers across 16 countries, and the finding that keeps sticking with me isn't the one about AI. It's this: 74% of engineering teams rate themselves in the "middle" of the observability maturity scale. Not reactive, not strategic. Stuck in the middle. They have dashboards, they have tracing, they have alerts. And yet when something goes wrong, they still can't tell you why ...

In MEAN TIME TO INSIGHT Episode 25, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses  AI's impact on the Wide Area Network (WAN) ... 

Application performance monitoring (APM) dashboards are only as useful as what they are configured to measure. The default setup covers obvious failure modes such as downtime, error spikes, and latency breaches, but it does not cover everything. Some failures produce no alerts or anomalies. The dashboard stays green while users experience a broken product. Here are six signs that is happening ...

The race to deploy AI is largely over. Most enterprises have entered it. The question now is not whether artificial intelligence is running inside the organization. The question is whether anyone is genuinely responsible for what it does. That is not a technical question. It is a leadership one. And most organizations are not yet structured to answer it honestly ...

A new analysis of 250 real-world queries across common retail tasks, such as product pricing, availability, ratings, shipping and specifications, reveals systemic inefficiency at the heart of web-based AI agents. On average, 97.9% of the data retrieved by agents from live web pages is irrelevant to the query being answered. Specifically, the average page ingested ran nearly 9,000 characters, while the average answer was just 32 characters, resulting in a noise-to-signal ratio of 278:1. Price queries were the most extreme outlier, with noise rates approaching 99.5%. That's not a rounding error. That's a structural problem ...

The enterprises that will define the next decade are not the ones that deployed the most technology. They are the ones who understood what their technology was actually doing. That distinction is not a philosophical point. It is the central operational challenge facing every organization that has spent the last five years modernizing at speed ...

AI is becoming the operating system of the enterprise. It acts as an invisible coordination layer that understands intent, connects systems, and executes work across complex SaaS environments. Previously, employees had to click through multiple systems — CRM, ERP, support tools, collaboration platforms — to complete a single task. Now, instead of navigating each application manually, they can simply state what they need to accomplish ...