Efficient Algorithms for Large-Scale Data Processing
A Comparative Study of MapReduce and Stream Processing Paradigms
Rohit |
This paper presents a comprehensive analysis of distributed data processing algorithms, comparing batch processing frameworks like MapReduce with modern stream processing systems. We evaluate performance characteristics, fault tolerance mechanisms, and practical applications across various workloads. Our findings suggest that hybrid approaches combining both paradigms offer the best balance of throughput, latency, and reliability for most real-world applications.
distributed systems data processing MapReduce stream processing