Efficient distributed computing framework and algorithm optimization for big data processing

With the rapid development of big data technology, the scale and complexity of data processing are increasing, and traditional computing frameworks and algorithms face performance bottlenecks when processing big data. Distributed computing frameworks have become the core technology for solving big data processing problems due to their efficient computing capability and good scalability. This paper first introduces the basic concepts and development status of distributed computing frameworks, and analyzes the advantages and shortcomings of current mainstream frameworks. It focuses on the optimization of algorithms for big data processing, including the optimization of data storage and management, the optimization of computing task scheduling and load balancing, and the improvement of parallel computing performance. Combined with practical application scenarios, the effects of different optimization strategies are evaluated, and possible directions for future development are proposed. The research in this paper provides theoretical basis and practical guidance for improving the efficiency of big data processing and reducing the cost of computing.