Having a stunning website with enough turnover doesn’t imply that it's successful in all aspects. To be more precise, one who closely analyse their venture can predict what's the future of it. We can definitely say you have analytics issues if you can’t answer questions like How many users come to your site each month? Whether that user traffic is trending up or down? etc.
To understand what is actually happening inside your server, web server logs are one of the most important source of information. A detailed and careful analysis of this data can help in system troubleshoots as well as understanding online user behavior. But it's not that much easy to process and understand useful informations from logs. To make it easier, there are several third party well structured tools available now. And the most popular one is the ELK Stack.
ELK Stack is the leading open-source IT log management solution which stands for Elasticsearch, Logstash and Kibana which are driven by the open-source vendor Elastic.
Elasticsearch is a highly scalable open-source analytics engine and well organised full-text search. It has the power to store, search, and analyze big volumes of data quickly. And it made Elasticsearch to be used as underlying engine/technology that have complex search features.
Logstash is an open source server-side data processing pipeline that intake data from different sources, extract/transform useful information and then put into a backend(Elasticsearch). Logstash has over 160 connector and transform-tools fetch and parse different types of logs available now.
INFO: Beats are great for gathering data. And if you want more processing muscle, Beats can also ship to Logstash for transformation and parsing.
Kibana is an open source (Apache Licensed), visualization tool for Elasticsearch. It's a widely used browser based application in which we can visualise, analyse and also can apply various types of search criteria on Elasticsearch data. This makes Kibana the default choice for visualizing data stored in Elasticsearch.
Logstash collects data/logs from various sources and passes it through certain data filter plugins(or transformation tools). To extract useful informations from log data, it must go through three stages say, inputs → filters → outputs. Inputs generate events that trigger the data intake, filters transform them, and outputs ship them to Elasticsearch. Actually filters are the functional part that process the log data. Logstash comes with more that 150 filter methods that can perform variety of operations on log data. And we can define how and what data need to be yielded from corresponding input log entry. Those data are finally outputted to Elasticsearch index which is responsible for providing all the search and analysis results.
These informations that we collect from logs are the valuable key factor in analytics. We can apply different types of search criteria on these data to get the results and transform it into actionable insights for your business.
Analytics done wisely can improve decision-making, lower risks and helps to understand how people are interacting with you online.