Java-Based Big Data Frameworks: Architecture, Challenges, and Future Directions - Eteng, Stephen (American International Journal of Computer Science and Technology, 2024)
The rapid growth of data generated by modern digital systems has increased the demand for scalable and efficient Big Data processing frameworks. Java-based Big Data frameworks have become central to addressing these demands due to their platform independence, robustness, and extensive ecosystem support. The purpose of this study is to examine the architecture, challenges, and future directions of widely adopted Java-based Big Data frameworks. This research employs a qualitative, literature-based methodology, analyzing peer-reviewed articles, technical documentation, and industry reports related to frameworks such as Apache Hadoop, Apache Spark, Apache Flink, and Apache Kafka. A comparative and thematic analysis approach is used to identify common architectural patterns, operational challenges, and emerging trends. The findings reveal that Java-based Big Data frameworks share layered, distributed architectures that emphasize fault tolerance and horizontal scalability. Key challenges include JVM-related performance overhead, memory management complexity, and system configuration difficulty. The study also identifies a clear shift toward real-time data processing, cloud-native deployment models, and tighter integration with machine learning workflows. In conclusion, Java-based Big Data frameworks remain foundational to modern data processing systems. Despite existing challenges, ongoing advancements in JVM technologies and distributed system design continue to enhance their performance and adaptability, ensuring their continued relevance in evolving Big Data environments.