Log data is a critical and many a times the only source to indentify the root causes of the problems or some critical business events. With business processes getting more and more complex, it is becoming increasingly difficult to rely on the manual approach of 'search and zero down' on the root cause of the problems. Cogniyug takes a whole new data driven approach to find the Root Causes of the critical events. Based on a patent pending 'pattern mining' algorithm, Cogniyug finds causal patterns providing quick insight into the various possible reasons behind the critical events affecting your business. This comes with innovative visualization and various useful statistical measures so that the Mean Time To Resolution (MTTR) is reduced from hours to seconds.
Using the knowledge of the patterns hidden in your data, Cogniyug opens up a whole new world of predictive intelligence so that you know about the critical events before they actually happen. You can literally search, select a message from search output and have Cogniyug predict it ! You can configure custom actions such as scripts or programs to execute when Cogniyug predicts critical events. Our IT and Telecom customers are using the predictive intelligence capability to prevent the downtime whereas our customers in Retail vertical are using it to grab business opportunities to cross-sell. We encourage you to read our case studies to know how different customers are exploiting 'predictive intelligence' of Cogniyug.
Life is repetitive and so are business processes. Events follow each other to form repetitive patterns. Certain things have to happen in certain order and must finish in certain time frames. These desired patterns depict the normal behaviour of the business processes. You can use 'Watch Points' capability of Cogniyug to watch for such patterns and get alerted or execute a custom script when deviations from the desired patterns are spotted by Cogniyug.
Diverse Data Sources
Cogniyug can consume the data from virtually any data source such as log files, syslogs, event logs, databases, web feeds or SNMP traps generated by machine sensors etc. Cogniyug ships many popular adapters that you can readily use to feed your data into Cogniyug. You can also write your custom adapters in any programming or scripting language of your choice to bring the data into Cogniyug. If you need any help for writing the adapters, please feel free to write to us on firstname.lastname@example.org
Cogniyug provides fantastic search capability with strong support for grammar and expressions. You can search with time relevance and locate the exact instance of the log message from terabytes of data.
Custom BI reports
Coupled with Cogniyug's search capability, you can select the data and process it externally in any programming or scripting language that you may know to produce custom BI reports on the data stored inside Cogniyug.
Cogniyug learns the behaviour of your business processes based on the data you feed and determines the normal behavioural patterns. It starts watching for the expected patterns and spots anomalies in real time. Cogniyug also provides an executive dashboard providing instantaneous health status of your business processes based on the data, anomalies seen and other vital statistics that it learns in real time.
Metadata is data about the data. Cogniyug supports metadata in simple key-value pair format and allows you to add as many key-value pairs as you like so that you can enrich your data. It forms vital part of the data as it helps you to correctly categorize the data, perform fast searches and visualize it better. Ideally, you add metadata at the time of data import (at the data adapter level) but you may also add the metadata at a later stage if you wish to do so.
Elastic Horizontal Scaling
Cogniyug is designed to scale horizontally. You can install every component of Cogniyug on a separate server or install all the components on a single server or install multiple instances of a component on multiple servers. Cogniyug architecture is pretty flexible. It allows you to add nodes when load increases and remove the nodes when load reduces.