AI-Driven Decision Making in the Age of Data Abundance: Navigating Scalability Challenges in Big Data Processing

Abstract

In an era where AI-driven decision-making is becoming increasingly important, the surge in data generation across different sectors poses significant scalability challenges for big data processing.This study delves into these challenges, aiming to enhance our understanding and management of large data volumes.It begins by stressing the critical importance of scalability in big data processing, highlighting its necessity for the functionality of data-centric applications today.The main challenges to scalability are then examined in the article, including problems with data storage, processing speed, and resource allocation.In order to assess how well distributed computing frameworks like MapReduce, Apache Spark, and Apache Hadoop can handle the growing needs of data processing, it offers a comprehensive examination of their performance.The study also broadens its scope to address how containerization and cloud computing technologies can help mitigate scaling issues.This research aims to provide an extensive overview of current technologies, frameworks, and techniques in order to tackle the scalability issues in big data processing in a complete manner.It seeks to ensure more efficient data processing techniques in the era of abundant information by advancing AI-driven decision-making capabilities in the face of expanding data quantities.