ADVANCEMENTS IN REAL-TIME STREAM PROCESSING: A COMPARATIVE STUDY OF APACHE FLINK, SPARK STREAMING, AND KAFKA STREAMS
Keywords:
Lambda Architecture, Stream Processing, Big Data Systems, Real-time Analytics, Distributed ComputingAbstract
This comprehensive article examines the evolution and implementation of Lambda Architecture in big data processing systems, focusing on its core components, implementation considerations, and relationship with the CAP theorem. The article analyzes how organizations have leveraged this architectural pattern to achieve significant improvements in data processing efficiency and system reliability. Through detailed examination of batch processing, speed layer operations, and serving layer integration, the article demonstrates the architecture's effectiveness in handling both historical and real-time data processing requirements. The article analysis encompasses critical implementation considerations, including technical challenges, system requirements, and operational constraints, while exploring the architecture's alignment with fundamental distributed system principles. Furthermore, the research investigates the emergence of modern alternatives such as Kappa Architecture and evaluates the impact of technological advancements on architectural evolution. By examining industry adoption patterns and future trends, this article provides valuable insights into the continuing transformation of data processing architectures and their role in meeting evolving business requirements.
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