jKool Blog

1 06, 2016

The 5 Best Methods for Drawing Insight out of Machine Data

By |June 1st, 2016|Big Data, Big Data Analytics, log analytics, machine learning|0 Comments

The pursuit of data-driven decision making has put tracking, logging and monitoring at the forefront of the minds of product, sales and marketing teams. Engineers are generally familiar with gathering and tracking data to maintain and optimize infrastructure and application performance. However, with the power of data, other business groups are clamoring for the […]

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 […]

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?

Everything.

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, […]

9 02, 2016

10 Reasons Your Application is Slow

By |February 9th, 2016|Apps, devops, end-user analytics, Java, log analytics, Real-time Analytics, Spark, storm, transaction tracking|0 Comments

1) Too much contention
You claim to have multi-threaded applications. Multi-threaded model does not mean faster. Your apps will not get the boost from multi-threaded model and multiple CPU/cores if you have too much contention, blocking and waiting. Review your threading model and weed out contention. Excessive contention will reduce your app performance as load […]

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 01, 2016

Why reducing MTTR is not enough

By |January 20th, 2016|Big Data, Big Data Analytics, cloud, devops, Java, log analytics|0 Comments

Reducing MTTR is a hot topic among DevOps practitioners.  MTTR measures average time for a cycle: problem occurrence, detection, response, and repair. Reducing the MTTR should greatly improve service quality right? Well, not exactly? The metric we should be looking at is this: what is the available time for repair (MATR — maximum available time […]

4 12, 2015

Log Analytics is DEAD

By |December 4th, 2015|devops, log analytics|0 Comments

Yes, did I really say that?? Yes I did. Log Analytics is a process of investigating logs and hoping to derive actionable information that might be useful to the business. Many log analytics tools are used to gain visibility into web traffic, security, application behavior, etc. But how valuable and practical is log analytics […]

11 11, 2015

Analyze and visualize your data with jKool on Bluemix

By |November 11th, 2015|Big Data Analytics, bluemix, Java, log analytics, Real-time Analytics, SaaS|0 Comments

Applications can behave in unexpected ways and it’s often difficult to know whether this is appropriate behavior or if something is really wrong. Sound familiar? This is the same conundrum parents face… and often just as hard to solve.

But, for Java developers and members of DevOps, the ultimate advantage in this situation would […]

5 10, 2015

How IoT can help in more efficient manufacturing

By |October 5th, 2015|Big Data Analytics, Internet of Things, log analytics, streaming analytics|0 Comments

Machines have aided factory workers and supervisors with precise shop floor data management, making the process of monitoring manufacturing plants easier, more efficient, and eventually increasing productivity rates…

 

jKool comments: solutions providing real-time analysis of log data from IoT sources in addition to monitoring metrics and transactions can provide tremendous insight into the data in […]

2 10, 2015

Live Q & A Transcript: Capture Real-Time Operational Intelligence from Big Data

By |October 2nd, 2015|Big Data Analytics, devops, log analytics, operational intelligence|0 Comments

If you missed the recent  Live Q and A with jKool CTO Albert Mavashev, get caught up with this transcript of the event.

The post Live Q & A Transcript: Capture Real-Time Operational Intelligence from Big Data appeared first on Data Informed.
Albert Mavashev: Hi, my name is Albert Mavashev. I am here to answer your questions about jKool, a platform for analyzing machine data in real-time, time series data like logs, metrics, transactions….
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