NoSQL Database: New Era of Databases for Big data Analytics - Classification, Characteristics and Comparison

TL;DR

This paper reviews NoSQL databases, classifying their types, characteristics, and comparing their suitability for big data analytics, helping organizations understand their strengths and weaknesses.

Contribution

It provides a comprehensive classification, analysis, and comparison of NoSQL databases tailored for big data analytics applications.

Findings

NoSQL databases handle large volumes of data effectively.

Different NoSQL types have distinct strengths and weaknesses.

The paper offers a clear evaluation framework for selecting NoSQL solutions.

Abstract

Digital world is growing very fast and become more complex in the volume (terabyte to petabyte), variety (structured and un-structured and hybrid), velocity (high speed in growth) in nature. This refers to as Big Data that is a global phenomenon. This is typically considered to be a data collection that has grown so large it can not be effectively managed or exploited using conventional data management tools: e.g., classic relational database management systems (RDBMS) or conventional search engines. To handle this problem, traditional RDBMS are complemented by specifically designed a rich set of alternative DBMS; such as - NoSQL, NewSQL and Search-based systems. This paper motivation is to provide - classification, characteristics and evaluation of NoSQL databases in Big Data Analytics. This report is intended to help users, especially to the organizations to obtain an independent understanding of the strengths and weaknesses of various NoSQL database approaches to supporting applications that process huge volumes of data.