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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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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 ...

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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 ...

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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 ...