Advanced Log Analytics for Better IT Management

Arunkumar Nair Jan 20 - 3 min read

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MSys Advanced Log Analytics

MSys Technologies’ lab is developing a Log Analytics tool which will collect logs, store the logs and do the analytics on the log data. The Log Analytics tool will be part of “Total Digital Transformation” application.  The “Total Digital transformation” is achieved through Dev-Ops, Continuous Integration, Continuous Delivery and Analytics. Log Analytics will ensure Digital Transformation is successful and effective.
Today Devops has transformed IT operations and IT Deployment. Now Analytics will transform DevOps and IT operations. DevOps – with Big Data analytics and machine learning key components of a successful IT operations. IT Managers and IT System admins will use powerful analytics and deeper insights at their fingertips to make effective business decisions.

Need for Log Analytics

One of the biggest hurdles in restoring service for a crashed application is to wade through the log files and identify the reason(s) for application failure.
Today many applications are getting deployed on many servers and in shorter time. The task of managing these applications and IT infrastructure is becoming complex and time-consuming. In case of business critical applications, any application down time results in loss of business and results in many escalations. Now it is possible to implement an analytics solution that can help in reducing the time needed to identify problems as and when they occur.
Initially, the Log Analytics will be used to raise alerts, sending email and/or creating tickets. The next step is to create a Log Analytic solution that can predict, with confidence, the occurrence of events of significance. Finally Log Analytics will have Predictive Analytics that has ability to identify and predict failures before they occur.

Advanced Log Analytics for Better IT Management

MSys Technologies has deep expertise in Log Analytics capabilities. Mys is building a Log Analytics using ELK stack of tools, where E stands for Elasticsearch, L stands for Logstash (log parser) and K stands for Kibana (visualization).
Elasticsearch, an open-source search engine is built on top of Apache Lucene™. It is a full-text search-engine library. Lucene is a complex, advanced, high-performance, and fully featured search engine library. Elasticsearch uses Lucene internally for all of its indexing and searching, but aims to make full-text search easy by hiding Lucene’s complexities behind a simple, coherent, RESTful API. Elasticsearch also supports the following features.


A distributed real-time document store where every field is indexed and searchable
A distributed search engine with real-time analytics
It is capable of scaling to hundreds of servers and petabytes of structured and unstructured data

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