
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
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 ...
Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...
Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...
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 ...
New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...
Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...
In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ...
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 ...