Role of Cloud Computing, Data Storage Scalability, and Distributed Computing Frameworks in Improving Big Data Processing Efficiency

Abstract

The rapid growth of digitized data in modern technological environments has made efficient big data processing a critical priority for organizations. As industries increasingly rely on data-driven decision-making, the role of enabling technologies such as cloud computing, data storage scalability, and distributed computing frameworks has become central. However, empirical evidence examining their combined impact in developing countries, particularly Pakistan, remains limited. This study addresses this gap by analyzing the influence of these technologies on big data processing performance within Lahore’s IT sector. A quantitative survey was conducted using a structured questionnaire with a five-point Likert scale, collecting data from 175 IT professionals, software developers, and data engineers. Data analysis was performed using IBM SPSS Statistics for descriptive analysis and reliability testing (Cronbach’s Alpha), alongside SmartPLS for Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings reveal that all three factors significantly and positively impact big data processing performance. Cloud computing emerged as the strongest predictor (β = 0.389, p < 0.001), followed by data storage scalability (β = 0.312, p < 0.001) and distributed computing (β = 0.274, p < 0.01). Reliability and validity measures confirmed strong model consistency, while the model explained 61.4% of variance (R² = 0.614). These results highlight the strategic importance of adopting scalable and cloud-based infrastructures to enhance big data capabilities in Pakistan’s emerging IT sector.