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.”
The Latest
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 ...
More data center leaders are reducing their reliance on utility grids by investing in onsite power for rapidly scaling data centers, according to the Data Center Power Report from Bloom Energy ...
In MEAN TIME TO INSIGHT Episode 21, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses AI-driven NetOps ...
Enterprise IT has become increasingly complex and fragmented. Organizations are juggling dozens — sometimes hundreds — of different tools for endpoint management, security, app delivery, and employee experience. Each one needs its own license, its own maintenance, and its own integration. The result is a patchwork of overlapping tools, data stuck in silos, security vulnerabilities, and IT teams are spending more time managing software than actually getting work done ...
2025 was the year everybody finally saw the cracks in the foundation. If you were running production workloads, you probably lived through at least one outage you could not explain to your executives without pulling up a diagram and a whiteboard ...