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jKool Announces Real-time Operational Intelligence for DataStax Enterprise

jKool announced support for real-time operational intelligence for DataStax Enterprise.

Starting today, DataStax Enterprise (DSE) users will be able to visualize and analyze operational, machine data streamed from their DataStax implementations on their jKool dashboard along with any other machine data they are viewing. Subscriptions to real-time updates from DataStax will provide users with proactive detection of performance and availability issues in their mission-critical, DSE platform which includes: the Apache Cassandra NoSQL database as well as Solr and Spark.

jKool’s real-time capability to spot problems and exceptions quickly and “find the needle in the haystack” provides immediate value to businesses using DSE as the platform for their applications exploiting the value in 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 responsible for DSE.

jKool support for DSE includes the following: visualization and analysis of the availability of the file-systems, operating system and memory and other critical resources used by DSE clusters. In addition jKool provides insight into runtimes, DSE tasks such as compaction, garbage collection, data ingestion or processing rates, reads, writes and dropped counts. jKool also ingests DSE logs and can detect hung tasks and many other exceptions that impact DSE cluster performance and availability. Any abnormal condition or trend towards one can be detected. jKool can be used concurrently with DSE OpCenter.

The jKool SaaS platform automatically visualizes time-series data from sources such as Apache Spark, STORM, Syslog, Log4j, 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.

“In today’s data-driven world, enterprises need to quickly capitalize on the data contained in their operational database systems to make decisions to better serve customers and drive business,” said Matt Pfeil, Chief Customer Office and co-founder, DataStax. “jKool provides users with a visual analytical solution to quickly capture and explore insight in their IT environment.”

“To date, operational intelligence is normally accomplished the 'old fashioned' way, poring over multiple logs, writing notes and trying to make sense of tasks that span across applications and servers. 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 add our support of operational intelligence for DataStax Enterprise, providing real-time visualization for all DSE metrics, performance and availability data and trends. DataStax clients can now have the same insight into their machine data as other applications.”

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jKool Announces Real-time Operational Intelligence for DataStax Enterprise

jKool announced support for real-time operational intelligence for DataStax Enterprise.

Starting today, DataStax Enterprise (DSE) users will be able to visualize and analyze operational, machine data streamed from their DataStax implementations on their jKool dashboard along with any other machine data they are viewing. Subscriptions to real-time updates from DataStax will provide users with proactive detection of performance and availability issues in their mission-critical, DSE platform which includes: the Apache Cassandra NoSQL database as well as Solr and Spark.

jKool’s real-time capability to spot problems and exceptions quickly and “find the needle in the haystack” provides immediate value to businesses using DSE as the platform for their applications exploiting the value in 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 responsible for DSE.

jKool support for DSE includes the following: visualization and analysis of the availability of the file-systems, operating system and memory and other critical resources used by DSE clusters. In addition jKool provides insight into runtimes, DSE tasks such as compaction, garbage collection, data ingestion or processing rates, reads, writes and dropped counts. jKool also ingests DSE logs and can detect hung tasks and many other exceptions that impact DSE cluster performance and availability. Any abnormal condition or trend towards one can be detected. jKool can be used concurrently with DSE OpCenter.

The jKool SaaS platform automatically visualizes time-series data from sources such as Apache Spark, STORM, Syslog, Log4j, 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.

“In today’s data-driven world, enterprises need to quickly capitalize on the data contained in their operational database systems to make decisions to better serve customers and drive business,” said Matt Pfeil, Chief Customer Office and co-founder, DataStax. “jKool provides users with a visual analytical solution to quickly capture and explore insight in their IT environment.”

“To date, operational intelligence is normally accomplished the 'old fashioned' way, poring over multiple logs, writing notes and trying to make sense of tasks that span across applications and servers. 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 add our support of operational intelligence for DataStax Enterprise, providing real-time visualization for all DSE metrics, performance and availability data and trends. DataStax clients can now have the same insight into their machine data as other applications.”

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Seeing is believing, or in this case, seeing is understanding, according to New Relic's 2025 Observability Forecast for Retail and eCommerce report. Retailers who want to provide exceptional customer experiences while improving IT operations efficiency are leaning on observability ... Here are five key takeaways from the report ...

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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