Sarcouncil Journal of Engineering and Computer Sciences

Abstract: The exponential growth of organizational data generation has positioned data processing paradigm selection as a critical architectural decision that fundamentally impacts business operations, system performance, and strategic outcomes. This comprehensive article examines the distinct characteristics, applications, and trade-offs between real-time and batch data processing approaches, providing practitioners with accessible frameworks for informed decision-making regardless of technical background. Through detailed comparative analysis, the article explores technical characteristics including data flow patterns, latency considerations, and resource utilization factors, while examining economic implications such as infrastructure costs and return on investment calculations. Case studies from healthcare monitoring systems and insurance analytics platforms illustrate practical applications where paradigm selection directly influences operational success and regulatory compliance. The article reveals that effective paradigm selection requires systematic evaluation of business requirements, technical infrastructure capabilities, and organizational constraints rather than technology-driven approaches. Emerging trends, including edge computing integration, machine learning pipeline automation, and event-driven architectures, demonstrate the evolving landscape where traditional paradigm boundaries become increasingly fluid. The article presents comprehensive decision frameworks that enable organizations to evaluate latency tolerance thresholds, data volume characteristics, and accuracy requirements while considering implementation complexity and long-term scalability needs. Cloud environment considerations encompass both streaming technologies and batch processing solutions, highlighting hybrid architectural approaches that combine paradigm strengths while minimizing individual limitations. Best practices and optimization techniques provide actionable guidance for robust pipeline design, common pitfalls avoidance, and future-proofing strategies that accommodate evolving business requirements. The convergence of real-time and batch processing capabilities through unified platforms suggests a future where organizations can dynamically adjust processing approaches based on data characteristics and business priorities, ultimately enabling more flexible and responsive data processing strategies that align with organizational objectives.