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Nastel Releases XRay 1.4

Nastel Technologies announced the immediate availability of the XRay AIOps and Tracing and Tracking solution's latest release.

XRay extracts and includes middleware messaging and other i2 data, combines it with data streams, log files, and other machine data, and also maps everything against time parameters to present a topology model of how the data and "transactions" flow through the entire enterprise application stack. Machine Learning / Artificial Intelligence (ML AI) is applied to compare the topology of a user's experience to the historical record of similar requests to identify the subtle, early indicators of a performance anomaly. Automation is applied for both rapid remediation, and pre-emptive actions as the system learn and improves.

This release includes enhancements to seamlessly support IoT infrastructures and integrations, containerized environments, and latest IBM MQ and Red Hat OpenShift updates, including:

- Red Hat OpenShift 4.9 and Red Hat Advanced Cluster Management for Kubernetes 2.4

- IBM MQ: Day 1 Support for IBM MQ 9.2.3, including leveraging new IBM MQ "Streaming Queues" to speed messages into analytics while maximizing performance

- XRay is updated to support OpenShift 4.9 and delivered via an OpenShift 4.9 container

Steven Menges, Head of Product Management at Nastel Technologies, said, "This AIOps and automation solution uniquely leverages data from the messaging middleware and integration infrastructure layer to provide critical intelligence from an organization's past and present investments in integration. Experts agree that enterprises who depend on these technologies should review Nastel's AIOps offering."

Also, enterprises that have invested in and rely on IBM MQ, IIB, ACE, Kafka, TIBCO EMS, and multi-middleware are impacted by enhancements in these areas:

User Experience (UX) and ML (machine learning) time-to-value enhancements:

- Predefined and customizable queries for business and other frequently used "views" are now canned for immediate usage such that the results are ready when you want them

- Automated ML "model training" scheduling

- Enhanced view and image sharing with a sharable URL for business, other users

- Automatically build/generate a set of related dashboards/views for ML for predictive analysis

IoT i2 Enhancements:

- Collect and stream IoT data to the XRay platform for analysis

- Inventory and do customizable visualization GEO mapping of IoT devices by type, location, status, etc.

"Nastel works closely with customers every day to understand how what they need is evolving," said Richard Nikula, VP of Research and Development at Nastel Technologies. "Our platform's architecture combined with our processes, automation, and cross-trained R&D team enables us to respond to requests and deliver new functionality and enhancements far more quickly than any of our competitors."

Nastel Technologies is also now releasing an enhanced version of Nastel Navigator, the leading i2M messaging middleware management, automation, and secure development "self-service" solution for application speed-to-market.

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Nastel Releases XRay 1.4

Nastel Technologies announced the immediate availability of the XRay AIOps and Tracing and Tracking solution's latest release.

XRay extracts and includes middleware messaging and other i2 data, combines it with data streams, log files, and other machine data, and also maps everything against time parameters to present a topology model of how the data and "transactions" flow through the entire enterprise application stack. Machine Learning / Artificial Intelligence (ML AI) is applied to compare the topology of a user's experience to the historical record of similar requests to identify the subtle, early indicators of a performance anomaly. Automation is applied for both rapid remediation, and pre-emptive actions as the system learn and improves.

This release includes enhancements to seamlessly support IoT infrastructures and integrations, containerized environments, and latest IBM MQ and Red Hat OpenShift updates, including:

- Red Hat OpenShift 4.9 and Red Hat Advanced Cluster Management for Kubernetes 2.4

- IBM MQ: Day 1 Support for IBM MQ 9.2.3, including leveraging new IBM MQ "Streaming Queues" to speed messages into analytics while maximizing performance

- XRay is updated to support OpenShift 4.9 and delivered via an OpenShift 4.9 container

Steven Menges, Head of Product Management at Nastel Technologies, said, "This AIOps and automation solution uniquely leverages data from the messaging middleware and integration infrastructure layer to provide critical intelligence from an organization's past and present investments in integration. Experts agree that enterprises who depend on these technologies should review Nastel's AIOps offering."

Also, enterprises that have invested in and rely on IBM MQ, IIB, ACE, Kafka, TIBCO EMS, and multi-middleware are impacted by enhancements in these areas:

User Experience (UX) and ML (machine learning) time-to-value enhancements:

- Predefined and customizable queries for business and other frequently used "views" are now canned for immediate usage such that the results are ready when you want them

- Automated ML "model training" scheduling

- Enhanced view and image sharing with a sharable URL for business, other users

- Automatically build/generate a set of related dashboards/views for ML for predictive analysis

IoT i2 Enhancements:

- Collect and stream IoT data to the XRay platform for analysis

- Inventory and do customizable visualization GEO mapping of IoT devices by type, location, status, etc.

"Nastel works closely with customers every day to understand how what they need is evolving," said Richard Nikula, VP of Research and Development at Nastel Technologies. "Our platform's architecture combined with our processes, automation, and cross-trained R&D team enables us to respond to requests and deliver new functionality and enhancements far more quickly than any of our competitors."

Nastel Technologies is also now releasing an enhanced version of Nastel Navigator, the leading i2M messaging middleware management, automation, and secure development "self-service" solution for application speed-to-market.

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Technology leaders across the federal landscape are facing, and will continue to face, an uphill battle when it comes to fortifying their digital environments against hostile and persistent threat actors. On one hand, they are being asked to push digital transformation ... On the other hand, they are facing the fiscal uncertainty of continuing resolutions (CR) and government shutdowns looming near and far. In the face of these challenges, CIOs, CTOs, and CISOs must figure out how to modernize legacy systems and infrastructure while doing more with less and still defending against external and internal threats ...

Reliability is no longer proven by uptime alone, according to the The SRE Report 2026 from LogicMonitor. In the AI era, it is experienced through speed, consistency, and user trust, and increasingly judged by business impact. As digital services grow more complex and AI systems move into production, traditional monitoring approaches are struggling to keep pace, increasing the need for AI-first observability that spans applications, infrastructure, and the Internet ...

If AI is the engine of a modern organization, then data engineering is the road system beneath it. You can build the most powerful engine in the world, but without paved roads, traffic signals, and bridges that can support its weight, it will stall. In many enterprises, the engine is ready. The roads are not ...

In the world of digital-first business, there is no tolerance for service outages. Businesses know that outages are the quickest way to lose money and customers. For smaller organizations, unplanned downtime could even force the business to close ... A new study from PagerDuty, The State of AI-First Operations, reveals that companies actively incorporating AI into operations now view operational resilience as a growth driver rather than a cost center. But how are they achieving it? ...

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...