Comparative Analysis of Data Storage Solutions for Responsive Big Data Applications

Abstract

The volume of produced digital data & everyday usage is increasing as a result of rising man-machine interaction, process automation, and falling hardware and software costs. The vast amount of digital information produced per second worldwide in unstructured, structured, and semi-structured formats is referred to as big data in this context. Big data analytics is a new topic that has inspired researchers all over the world to create, develop, and implement a variety of mechanisms, technologies, architectures, and areas for evaluating the enormous amount of data createdevery day. Traditional database management systems struggle to make sense of the massive amounts of data that make up "big data."The paper describes a few analyses, including word count, sentiment analysis, and responsive analysis.

Responsiveness is crucial for improving the system, identifying vulnerabilities, and ensuring that tasks are distributed fairly. Responsive is important data that can be used for making wise decisions. Responsive is significant for both strengths and weaknesses, not just when it identifies flaws. If the responsive analysis is performed incorrectly, the analysis's conclusion will likewise be incorrect. As a result, the system as a whole will be inaccurate because the pattern that was discovered will also be incorrect. We will utilize the Map-Reduce framework to create this suggested system for responsive analysis, and Hadoop will be used for storage.