https://iaeme.com/Home/journal/IJDARD 50 editor@iaeme.com International Journal of Data Analytics Research and Development (IJDARD) Volume 2, Issue 1, January-June 2024, pp. 50–58, Article ID: IJDARD_02_01_006 Available online at https://iaeme.com/Home/issue/IJDARD?Volume=2&Issue=1 Impact Factor (2024): 4.65 (Based on Google Scholar Citation) Journal ID: 97A6-7C77 © IAEME Publication COMPARATIVE ANALYSIS OF REAL-TIME AND BATCH DATA PROCESSING: TECHNOLOGIES, PERFORMANCE, AND USE CASES Sivanagaraju Gadiparthi Independent Researcher, NJ, USA Jagjot Bhardwaj Independent Researcher, MN, USA ABSTRACT In an era where data-driven decision-making predominates, selecting the appropriate data processing method is crucial for organizational efficiency and effectiveness. This article provides a comprehensive analysis of two principal data processing approaches: real-time data processing and batch processing. Both methods are dissected to illuminate their operational mechanics, advantages, disadvantages, and optimal use cases in various industries. Real-time data processing is characterized by its capability to process data instantaneously, thereby facilitating immediate decision-making crucial in sectors such as financial trading, emergency services, and online services. This method’s primary advantages include the ability to react swiftly to dynamic conditions and the potential for enhancing user interaction and satisfaction. However, the complexity and cost of establishing and maintaining a real-time processing system pose significant challenges, particularly with regards to scalability and technical infrastructure. Conversely, batch processing involves the collection and processing of data at predetermined intervals, allowing for the efficient management of large data volumes without the necessity for immediate output. This approach is particularly advantageous in scenarios where data processing can be deferred to off-peak hours, thus optimizing resource use and reducing operational costs. While batch processing is less suited for tasks requiring instant data availability, its reliability, simplicity, and cost-effectiveness make it ideal for comprehensive analytical tasks in industries such as retail, banking, and healthcare analytics.