jKool, LLC introduced a new, software-as-a-service (SaaS) solution for analyzing Big Data in real time.
JKool is designed to spot the patterns in data that lead to actionable insights. Often the value hidden in this data is perishable, requiring immediate action before it becomes stale. JKool’s near real-time analysis of time-series data is able to capture insight, instantly. After an initial preview period, jKool LLC. will introduce a paid subscription service that companies can use on a regular basis.
JKool provides a number of streaming operators as part of its service including: Bollinger Bands, aggregates and filters in order to automatically detect outliers and anomalies. It also considers that data can be location-specific with meaning that is location-explicit. Its geo-fencing capability is helpful in detecting patterns in behavior that are linked to individual geographies. JKool is different than other tools in its innate ease-of-use, open-source approach and high scalability. As a SaaS solution, there is nothing to install or maintain. The solution features a simple, mobile-friendly web dashboard and an English-like query language called JKQL that lets users converse ad hoc with their data and then interact with the visuals on the dashboard.
The service provides an open source API, TNT4J, which makes it easy to stream information into the jKool cloud service. JKool scales transparently, parallelizing natural-language queries in order to quickly reveal cause and effect, correlations between disparate data points, locations, bottlenecks, and outliers that are integral to how businesses perform.
“To date, business analytics has been reserved for the largest companies with the biggest budgets and a plethora of experts,” said Charley Rich, vice president of product management at jKool. “jKool offers big data analytics for the rest of us—businesses of all sizes and across industries. We’re inviting everyone to try the service, use its sample data, download the open source API and understand how they can really use their data, not simply save it.”
jKool designed the technology and service in response to the evident need from sectors such as financial services, healthcare and advertising to get at the business insights buried in timeseries data. These may include trends on how, when and where consumers are behaving in the purchasing process, how the market is reacting or trends in healthcare over time.
The Latest
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 ...
In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ...
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 ...
Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...