With the rapid advancement of big data technology, distributed architecture has become the mainstream in the industry when processing massive amounts of information. However, when dealing with such large datasets, the query efficiency and performance of the system become key factors that constrain its response speed and accuracy. This study analyzed the key performance bottlenecks of distributed data queries, such as storage response latency, hardware processing capacity limits, and data consistency assurance. Based on this, a series of targeted improvement measures were proposed. Specifically, in terms of distributed storage network latency, hardware resource upgrade requirements, and data consistency maintenance, research has proposed solutions such as optimizing data distribution, regularly upgrading hardware facilities, and adopting distributed locking strategies. After implementing these optimization measures, query response can be accelerated, data accuracy can be ensured, and hardware costs and maintenance expenses can be reduced. The research results show that these optimization methods can enhance the overall performance of processing large-scale data systems.

Jin Li. The Impact of Distributed Data Query Optimization on Large-Scale Data Processing. International Journal of Big Data Intelligent Technology (2025), Vol. 6, Issue 2: 139-146. https://doi.org/10.38007/IJBDIT.2025.060215.

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