Make Apps Faster, End Users HappierCharley Rich2016-07-21T22:19:35+00:00
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
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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