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SL Announces Monitoring as a Service for Apache Kafka

SL is now offering Monitoring as a Service for middleware technologies with initial availability for monitoring Apache Kafka with RTView Cloud.

RTView Cloud provides users with a way to proactively monitor their complex Kafka environments to maximize uptime and reduce the time required to address incidents. The solution consists of pre-built dashboards for monitoring Kafka brokers, producers, consumers, topics and zookeepers. With dozens of pre-defined alerts and pre-built monitoring displays, users can quickly deploy a powerful monitoring solution without the time, skill and expense necessary to build or configure their own monitoring applications.

The new RTView Cloud designer provides additional capability for users to create custom monitoring displays from a browser and without the need to do any custom programming. This enables middleware support teams to offer custom displays to their end users that provide them with the exact metrics they need in the exact way they would like to see them.

Data security with RTView Cloud is enhanced through the use of a hybrid architecture which ensures all monitoring data stays securely behind the firewall. Performance metrics and alert data are accessed directly from users’ browsers and never pushed out to the Cloud.

“Apache Kafka is being adopted by many of our integration customers,” said Tom Lubinski, CEO of SL. “We are excited to announce support for monitoring Kafka as a service and believe this will appeal to many of our customers because of the ease of implementation.”

SL, with RTView Enterprise Edition, fully supports monitoring for TIBCO and Solace middleware. RTView Cloud support for TIBCO and Solace is available currently for beta users.

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SL Announces Monitoring as a Service for Apache Kafka

SL is now offering Monitoring as a Service for middleware technologies with initial availability for monitoring Apache Kafka with RTView Cloud.

RTView Cloud provides users with a way to proactively monitor their complex Kafka environments to maximize uptime and reduce the time required to address incidents. The solution consists of pre-built dashboards for monitoring Kafka brokers, producers, consumers, topics and zookeepers. With dozens of pre-defined alerts and pre-built monitoring displays, users can quickly deploy a powerful monitoring solution without the time, skill and expense necessary to build or configure their own monitoring applications.

The new RTView Cloud designer provides additional capability for users to create custom monitoring displays from a browser and without the need to do any custom programming. This enables middleware support teams to offer custom displays to their end users that provide them with the exact metrics they need in the exact way they would like to see them.

Data security with RTView Cloud is enhanced through the use of a hybrid architecture which ensures all monitoring data stays securely behind the firewall. Performance metrics and alert data are accessed directly from users’ browsers and never pushed out to the Cloud.

“Apache Kafka is being adopted by many of our integration customers,” said Tom Lubinski, CEO of SL. “We are excited to announce support for monitoring Kafka as a service and believe this will appeal to many of our customers because of the ease of implementation.”

SL, with RTView Enterprise Edition, fully supports monitoring for TIBCO and Solace middleware. RTView Cloud support for TIBCO and Solace is available currently for beta users.

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While 87% of manufacturing leaders and technical specialists report that ROI from their AIOps initiatives has met or exceeded expectations, only 37% say they are fully prepared to operationalize AI at scale, according to The Future of IT Operations in the AI Era, a report from Riverbed ...

Many organizations rely on cloud-first architectures to aggregate, analyze, and act on their operational data ... However, not all environments are conducive to cloud-first architectures ... There are limitations to cloud-first architectures that render them ineffective in mission-critical situations where responsiveness, cost control, and data sovereignty are non-negotiable; these limitations include ...

For years, cybersecurity was built around a simple assumption: protect the physical network and trust everything inside it. That model made sense when employees worked in offices, applications lived in data centers, and devices rarely left the building. Today's reality is fluid: people work from everywhere, applications run across multiple clouds, and AI-driven agents are beginning to act on behalf of users. But while the old perimeter dissolved, a new one quietly emerged ...

For years, infrastructure teams have treated compute as a relatively stable input. Capacity was provisioned, costs were forecasted, and performance expectations were set based on the assumption that identical resources behaved identically. That mental model is starting to break down. AI infrastructure is no longer behaving like static cloud capacity. It is increasingly behaving like a market ...

Resilience can no longer be defined by how quickly an organization recovers from an incident or disruption. The effectiveness of any resilience strategy is dependent on its ability to anticipate change, operate under continuous stress, and adapt confidently amid uncertainty ...

Mobile users are less tolerant of app instability than ever before. According to a new report from Luciq, No Margin for Error: What Mobile Users Expect and What Mobile Leaders Must Deliver in 2026, even minor performance issues now result in immediate abandonment, lost purchases, and long-term brand impact ...

Artificial intelligence (AI) has become the dominant force shaping enterprise data strategies. Boards expect progress. Executives expect returns. And data leaders are under pressure to prove that their organizations are "AI-ready" ...

Agentic AI is a major buzzword for 2026. Many tech companies are making bold promises about this technology, but many aren't grounded in reality, at least not yet. This coming year will likely be shaped by reality checks for IT teams, and progress will only come from a focus on strong foundations and disciplined execution ...

AI systems are still prone to hallucinations and misjudgments ... To build the trust needed for adoption, AI must be paired with human-in-the-loop (HITL) oversight, or checkpoints where humans verify, guide, and decide what actions are taken. The balance between autonomy and accountability is what will allow AI to deliver on its promise without sacrificing human trust ...

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