Unlock Insights in Machine Data with jKool Analytics

jKool is about turning raw machine data into actionable insight. Learn about patterns, trends, behavior of your applications and end-users.

Improve user experience and improve application performance with jKool by consolidating and analyzing logs, performance, transactions and more.

Use jKool Stream Processing Language (JKQL) to search across all your data and turn it into visual charts, graphs, maps and topologies.

jKool is available as SaaS or on-premises.

Unified Application Analytics


Register below for a jKool Free Account, stream data and get insight into hidden patterns. No credit card required.

This is a sample of an IT Ops dashboard visualizing data collected from Log4j. As logs are updated, the data is streamed to jKool where it is analyzed and visualized, in-memory, in real-time.

Forensics for your Application

  • Analyze logs, performance and improve service quality
  • Query all your data using english like query langauge (JKQL)
  • Visualize, trends, patterns and anomalies on a web-based, mobile-ready dashboard
  • Application, server and geographic maps, topology

Track Transactions and End-user Experience

  • Measure end-user experience, response time and behavior.
  • Determine causality for slow, failed transactions both short and long running.
  • Detect anomalies in transaction flow and performance – before your end-users do.
  • Determine which user are affected and why — browser to server
The Viewlets show two different views of a “stiched” transaction flow. The upper Viewlet shows the flow on a geographical map, while the bottom Viewlet shows this in an application topology view.
Analyze application usage in real-time as your users engage with your app or historical based on prior engagements. Find trends, patterns, spikes, anomalies, least/most/best/worst.

Application & End-user Analytics

  • Analyze your application usage: best/worst or most/least common features
  • Measure how your end-user use your application, subcriptions, cancellations
  • Make data driven decisions based on how your customers use your app
  • Measure your application performance and quality of service

Transaction and Log Analytics

  • Consolidate, aggregate and analyze logs, transactions
  • Search and compare all your data from any source
  • Detect error rate over time, measure response time
  • Analyze end-user behavior, actions over time

The Viewlet at the top shows an analysis of time-series events using Bollinger Bands and Exponential Moving Averages (EMAs).

The Viewlet on the bottom is a comparison showing the differences between events.


jKool scales to handle the largest transaction rates providing real-time visualization and analytics of streaming, time-series data.

jKool FatPipes micro services architecture is a clustered compute platform for ingesting and analyzing machine data in motion and at rest. FatPipes implements Lambda architecture and makes use of open source tech such as Apache Cassandra, Solr, STORM, Spark and others.

Extreme Scalability & Flexibility

  • Built on NoSQL & clustered computing plaform for elastic scalability
  • Streaming analytics using Complex Event Processing (CEP)
  • Multi-tenant, multi-streams, multi-dashboards
  • No schemas, no infrastructure to deploy, no data to manage
jKool-Operational-Intelligence-DatasheetDATASHEET: jKool Operational Intelligence
Read Data Sheet

jKool Does Machine Data In Motion

Streaming Analytics

  • Automatic sequence, order, group and store of data in motion
  • In-flight data queries and aggregations for sub-second reponse
  • TTL: assign time-to-live to your data and have it auto-expire
  • Store only what is relevant and needed for forensics


  • Transaction splitting & morphing
  • Event causality, topology, root-cause
  • Chain related events, transactions, metrics
  • Auto-discover application and transaction topology


  • Compute elapsed time across event chains (ev1…evN)
  • Anomaly detection: normal vs abnormal behavior
  • Detect outliers without knowing what they are
  • Compute & aggregate data-in-motion & data-at-rest

Aggregations, Anomalies

  • High/low bands, outliers, counts (max, min, avg) and more
  • Time Interval Bucketing (second, min, hour), grouping
  • Extensive library of math, string and other functions
  • Compare events, transactions (Ev1…EvN)

Analyze Data Streams

  • Analyze machine data streams in motion
  • Aggregate streams based on sliding time windows
  • Apply filters, expressions on streaming data
  • Display in-flight computations on the dashboard

Use our open-source data collectors to analyze your application performance, end-user experience and service quality.

Common Use Cases

Fix Problems Faster
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Fix Problems Faster
Real time visibility results in faster problem resolution.

Go beyond logs & monitor metrics to eliminate false alarms.

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Boost Team Productivity
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Boost Team Productivity
Spend less time patching & more time innovating by creating new apps.

Scale App teams by reducing application support overhead.

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Be Proactive
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Be Proactive
Spot trends and detect patterns in order to prevent problems before they impact customers.
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Turn machine data into insight. Consolidate & analyze logs, performance and transactions.