Case Study: Splunk Vs. Nagios

Splunk vs Nagios
What is Splunk?
Splunk is a tool to analyze, search and visualize machine data. Today, with the increasing popularity of internet, IoT devices, wearable devices and multi-fold increase in computation and processing power, generation of machine data has increased exponentially. Such huge amount of machine data contains powerful insights which might have tremendous value. Many big data technologies and tools are being developed to extract benefit from such data, Splunk is one such tool.
Founded in 2003, Splunk offers its products across three primary categories, Splunk Enterprise, Splunk Light and Splunk Cloud.
The primary purpose of Splunk is to collect and analyze high volume of machine data (big data) and identify data patterns, provide metrics,
Data that comes reaches to indexer first, where it is indexed and then using searcher, one can search for the relevant keywords. After the searcher stage, a knowledge object can be set which makes the operation smarter and it brings intelligence to the system. These knowledge objects monitor events and give notifications when certain conditions occur. These results can be collected and visualized by creating reports and time-charts.
Knowledge objects are essentially user defined and is used for extracting knowledge for existing or run time data to enrich the data analysis process. There are three main types of knowledge objects in Splunk: Splunk time-charts, data models and alerts.

Splunk can be configured to use IoT data. For example, it is possible that Splunk gets data from wearable devices using IoT technologies to monitor health parameters of patients and send notifications regarding critical changes in conditions to doctors as well as patients. Thus, it can report health conditions in real time, delve deeper into patient’s health records and analyze them and generate period or need based alerts to concerned persons.

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