It is Thursday again – we know what that means? No? You forgot? Nemeon’s Thursday Technical of course! In a mini-series, our analyst Pragati Khadka explores the wonderful world of log monitoring platforms.

A short note on what these platforms are: “A log monitoring platform is a software tool that allows users to collect, store, and analyze log data generated by various systems, applications, and services. The platform provides a centralized location for monitoring and analyzing data, allowing users to identify issues, troubleshoot problems, and improve system performance.”

In this first episode of our three-part series on log monitoring, we’ll focus on AWS OpenSearch Service.

Short Description of Amazon OpenSearch Service

Amazon OpenSearch service is an AWS-managed service that can be used to search, visualize, analyze up to petabytes of text and unstructured data. It is based on OpenSearch which is a distributed and open-source search and analytics suite. OpenSearch provides a highly scalable system for providing fast access and response to large volumes of data with an integrated visualization tool, OpenSearch Dashboards, that makes it easy for users to explore the data. OpenSearch can be used as log management engine because it stores the index and processes the log based on the query sent to the engine by the user.

Most Important Use Cases

At a higher level, there are two important use cases of OpenSearch service namely Log analytics and search.

Log analytics involves searching, analyzing, and visualizing machine data generated by IT systems and technology infrastructure to gain operational insights. It can be used for monitoring and debugging applications. For example, what is the latency and error rate? What error code has occurred which caused the issue in the application? Is the infrastructure working as expected? In case of security, it protects application and services against fraud and denial of service (ddos) attacks.

Search is used to find the right product, service, document or answer quickly across semi-structured and unstructured data. This allows to provide personalized recommendations. For example, in ecommerce platform customers can find the right product quickly and customer engagement can be increased by delivering personalized recommendations as per their interests.