In the era of big data, efficient query processing is paramount for organizations seeking valuable insights from vast and diverse datasets. This paper presents a comprehensive comparative analysis of various query processing techniques tailored for big data environments. The study evaluates the performance, scalability, and adaptability of these techniques, shedding light on their strengths and weaknesses. Ultimately, this research serves as a valuable resource for data architects, engineers, and analysts faced with the task of selecting the most appropriate query processing technique for their big data projects. It provides insights into the trade-offs and considerations necessary to harness the full potential of big data analytics while optimizing resource utilization and query response times. The evaluation is conducted through a series of experiments on benchmark datasets and workloads that mimic real-world scenarios.
Keywords: Big Data
