Distributed Data Processing at Scale: A Review of Key Frameworks and Their Characteristics

• Trinath Patri,

Vol. 20, No. 1, January 31, 2026
10.3837/tiis.2026.01.005, Download Paper (Free):

• Distributed Data Processing
• Big data
• Apache Hadoop
• Apache Spark
• Real-Time Analytics.

Abstract

The exponential development of data has outperformed conventional data processing systems, creating a necessity for more scalable, efficient, and flexible solutions. To address this, distributed data processing frameworks have emerged as the key enablers, allowing organizations to manage and analyze massive datasets across multiple machines in parallel. Frameworks such as Apache Hadoop, Apache Spark, Apache Flink, Apache beam, Apache Samza, Dask and Apache Kafka are designed to handle the challenges of large-scale data processing by offering fault tolerance, scalability, and support for both batch and real-time analytics. Apache Hadoop, based on the MapReduce paradigm, excels in batch processing of large static datasets, while Apache Spark extends these capabilities through in-memory computation and unified support for batch and streaming data. Apache Flink, in contrast, specializes in low-latency stream processing, making it ideal for real-time and event-driven applications. In this study, these frameworks were systematically compared and analyzed based on key parameters such as processing speed, scalability, fault tolerance, resource utilization, and ease of integration with ML and cloud platforms. Experimental and literature-based evaluations were conducted to highlight their relative strengths and limitations across diverse use cases. Furthermore, the rapid evolution of cloud computing and serverless architectures has enhanced the deployment flexibility of these frameworks, enabling organizations to scale their computational power dynamically without extensive infrastructure management. The analysis concludes that while each framework has unique advantages, their combined adaptation, integrating Spark’s versatility, Hadoop’s robustness, and Flink’s real-time performance represents the future direction of distributed big data processing. As data volumes continue to grow exponentially, understanding these frameworks remains crucial for organizations to harness large-scale data for insight and innovation.

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