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jKool Announces Real-Time Operational Intelligence for Java Deployments

jKool announced support for real-time, operational intelligence for Java deployments.

Users will be able to analyze and visualize real-time machine data streams from Java applications and system log files on a jKool dashboard along with other application metrics and transactions. Subscriptions to real-time updates from infrastructure systems will provide users with proactive detection of performance, availability and capacity issues in their mission-critical applications before they impact the business. jKool’s real-time visualization and analytics enables users to learn what they didn’t already know and capture perishable insights important to their business.

The productivity of IT Ops, DevOps and Application Support is improved by jKool’s consolidation of operational data from multiple applications, servers and tiers onto a single web-based, mobile-ready dashboard. This simplification in process can greatly reduce the time to diagnose a problem resulting in reduced costs and improved customer experience. The jKool Operational Intelligence solution analyzes and aggregates logs and metrics, tracks and traces Java transactions and provides real-time visualization with extreme scalability.

jKool’s real-time capability to spot problems and exceptions quickly and “find the needle in the haystack” provides immediate value to businesses whose applications are exploiting the value in the Digital Healthcare, Retail, Financial Services, the Internet of Things (IoT), and other Web and Mobile applications. jKool’s instant insight helps reduce risk and can immediately improve the productivity of the DevOps and application support teams.

The jKool SaaS platform automatically visualizes time-series data from sources such as Apache Spark, STORM, Log4j, Syslog, Logback, SLF4J, JMX or Java EE in real-time. Using SaaS, there are no servers, database or schemas to manage. jKool’s real-time scorecard provides immediate insight to developers or users in DevOps who can now make split-second decisions based on data in motion about their business-critical applications and infrastructure. They learn what they didn’t already know, take advantage of perishable insights, avoid preventable losses and uncover new opportunities.

jKool visualization includes behavior, performance, location and topology displayed on a real-time dashboard. Users can subscribe to topics of interest and get updates as they arrive or interactively ask questions using an easy-to-use, English-like query language (jKool Query Language or jKQL). jKool, a multi-tenant solution is itself built on the Apache open-source foundation of Cassandra and Solr from DataStax, along with Kafka, Spark and STORM all orchestrated in-concert via jKool FatPipes™ micro services technology. To make data ingestion easy and flexible, jKool provides a library of open-source collectors.

"To date, operational intelligence is normally accomplished via “heavy lifting”, manually attempting to relate events in one log with another and struggling to make sense of tasks that span across applications, servers and tiers. This is a tedious process with low productivity and high risk. jKool’s real-time visualization and analytics can provide instant insight into operational, machine data in real-time improving productivity, reducing risk and increasing application quality,” said Charley Rich, VP of Product Management at jKool. “Today, we are excited to announce our support of a SaaS operational intelligence solution, providing real-time visualization of Java logs, system logs, transactions, performance, availability and other user-defined metrics and trends.”

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jKool Announces Real-Time Operational Intelligence for Java Deployments

jKool announced support for real-time, operational intelligence for Java deployments.

Users will be able to analyze and visualize real-time machine data streams from Java applications and system log files on a jKool dashboard along with other application metrics and transactions. Subscriptions to real-time updates from infrastructure systems will provide users with proactive detection of performance, availability and capacity issues in their mission-critical applications before they impact the business. jKool’s real-time visualization and analytics enables users to learn what they didn’t already know and capture perishable insights important to their business.

The productivity of IT Ops, DevOps and Application Support is improved by jKool’s consolidation of operational data from multiple applications, servers and tiers onto a single web-based, mobile-ready dashboard. This simplification in process can greatly reduce the time to diagnose a problem resulting in reduced costs and improved customer experience. The jKool Operational Intelligence solution analyzes and aggregates logs and metrics, tracks and traces Java transactions and provides real-time visualization with extreme scalability.

jKool’s real-time capability to spot problems and exceptions quickly and “find the needle in the haystack” provides immediate value to businesses whose applications are exploiting the value in the Digital Healthcare, Retail, Financial Services, the Internet of Things (IoT), and other Web and Mobile applications. jKool’s instant insight helps reduce risk and can immediately improve the productivity of the DevOps and application support teams.

The jKool SaaS platform automatically visualizes time-series data from sources such as Apache Spark, STORM, Log4j, Syslog, Logback, SLF4J, JMX or Java EE in real-time. Using SaaS, there are no servers, database or schemas to manage. jKool’s real-time scorecard provides immediate insight to developers or users in DevOps who can now make split-second decisions based on data in motion about their business-critical applications and infrastructure. They learn what they didn’t already know, take advantage of perishable insights, avoid preventable losses and uncover new opportunities.

jKool visualization includes behavior, performance, location and topology displayed on a real-time dashboard. Users can subscribe to topics of interest and get updates as they arrive or interactively ask questions using an easy-to-use, English-like query language (jKool Query Language or jKQL). jKool, a multi-tenant solution is itself built on the Apache open-source foundation of Cassandra and Solr from DataStax, along with Kafka, Spark and STORM all orchestrated in-concert via jKool FatPipes™ micro services technology. To make data ingestion easy and flexible, jKool provides a library of open-source collectors.

"To date, operational intelligence is normally accomplished via “heavy lifting”, manually attempting to relate events in one log with another and struggling to make sense of tasks that span across applications, servers and tiers. This is a tedious process with low productivity and high risk. jKool’s real-time visualization and analytics can provide instant insight into operational, machine data in real-time improving productivity, reducing risk and increasing application quality,” said Charley Rich, VP of Product Management at jKool. “Today, we are excited to announce our support of a SaaS operational intelligence solution, providing real-time visualization of Java logs, system logs, transactions, performance, availability and other user-defined metrics and trends.”

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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 ...

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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 ...

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