jKool Blog

16 05, 2016

The Internet of Things and Manufacturing Operations Management

By |May 16th, 2016|Internet of Things, operational analytics|0 Comments

More than 40 percent of organizations expect the Internet of Things (IoT) to transform their business or offer significant new revenue or cost-savings opportunities over the next three years, according to industry analyst firm Gartner.

This makes it all the more important that manufacturers have a solid understanding of the differences between MOM systems and […]

25 02, 2016

3 Reasons Root Cause Analysis is Fundamentally Flawed

By |February 25th, 2016|devops, Java, operational analytics, prediction, Real-time Analytics|0 Comments

Always the same final answer to all WHYs

Root-cause analysis = ask WHY until the answer is ‘i don’t know’, meaning that root-cause analysis inevitably arrives @ UNKNOWN or UNKNOWABLE:

Oversimplified example:

Boss: Our application is slow and end-users are unhappy, why?
You: Database is slow
Boss: Why?
You: Because our report query is taking too long.
Boss: Why?
You: Database server is out of memory.
Boss: Why???
You: I think Bob […]

23 02, 2016

5 ways to spice up DevOps with jKool

By |February 23rd, 2016|blog, devops, Java, log analytics, operational analytics, operational intelligence, performance monitoring, SaaS, streaming analytics, transaction tracking|0 Comments

Eliminate finger pointing, reduce blame

jKool allows application owners to track and audit transactions; from browser all the way to application servers and middleware. Ability to track transactions is critical if you want to know if your app is slow; and where and why it is slow. How often is the blame placed on you or your team […]

22 02, 2016

Gartner shakes up annual ranking of business analytics tools – TechTarget

By |February 22nd, 2016|Data Science, operational analytics, Real-time Analytics, Spark, streaming analytics|0 Comments

Open source tools, such as R, Python and Spark, are growing in prominence. The number of data sources available to businesses is increasing. And the need for complex event processing and
streaming analytics related to the Internet of Things is growing.

[…] Read the source article at searchbusinessanalytics.techtarget.com
Original Author: edburnstt

11 02, 2016

What does APM have to do with Fast Data?

By |February 11th, 2016|Apps, Big Data, Big Data Analytics, Fast Data, Java, log analytics, operational analytics, operational intelligence, performance monitoring, Real-time Analytics, streaming analytics, transaction tracking|0 Comments

Fast Data is about processing high velocity data in real-time as it happens. Think of Fast Data as Big Data in real-time.

So what does APM have to do with Fast Data?


APM is all about processing lots and lots of data as close as possible to real-time.
Tracking transactions, analyzing logs, sampling metrics, figuring out relationships, […]

22 01, 2016

Operational Analytics Seen Boosting IT Performance

By |January 22nd, 2016|Big Data Analytics, log analytics, operational analytics, operational intelligence|0 Comments

The industry-sponsored report compiled by market researcher Enterprise
Management Associates (EMA) noted that investments in operations analytics and the
management of application performance could be applied across multiple use cases. One reason …

[…] Read the source article at EnterpriseTech
Original Author: EnterpriseTek

20 06, 2015

Learn How to Unlock Big Data:

By |June 20th, 2015|Big Data, Big Data Analytics, operational analytics|0 Comments

Spot the Patterns in your Data that Lead to Actionable Insights

jKool, a SaaS solution for real-time analytics, forensics and transaction tracking provides instant insight from fast data, making it easy to detect the hidden patterns and anomalies that offer opportunities for business value.

Big data promises a lot, yet few businesses have mastered what it takes turn this data into actionable insights. Boardrooms see the value in analytics or business intelligence (BI) as a way to make business predictions and gain a competitive advantage. Many of the largest enterprises spend millions on database farms, DataMarts and even proprietary hardware to accomplish this. Often their output is produced in batch and unable to keep up with the velocity of their business. Even with all that expense and technical know-how, these firms are limited when it comes to very largest data volumes – Big Data. However smaller firms with limited budgets are unable to afford BI and do not have the resources financial and technical to take advantage of NoSQL. In addition these companies would like to move beyond batch and extract useful information from high velocity data streams in near real-time. […]