Efficient Data Access and Streamlined Retrieval: Enhancing Performance in Cloud-Based Systems

Accessing data from cloud storage has become an essential part of information management since it simplifies data retrieval and manipulation. However, along with this ease comes possible latency problems and the requirement for quick data retrieval mechanisms. To improve user interactions with a data repository, this work presents a comprehensive system architecture. Specifically, performance evaluation utilizing the “Global Organization Repository (GOR)” and the “Retail Sales Benchmark dataset” is the main focus of this research. The Query Transformation Layer is crucial since it translates user-input SQL queries into HiveQL, facilitating effective communication with the Hive Data Repository Layer. The purpose of this layer is to simplify the intricacies of HiveQ Land also utilize a technique for parsing and translating queries. Assessment of the Hive framework highlights difficulties in retrieving data and user satisfaction. The existence of diverse datasets, such as GOR, highlights the necessity for precise query translation. The research improves system performance by addressing the problems and proposing contemporary techniques. The objectives are to improve the Query Transformation Layer to make it more user-friendly, reduce the difficulty in learning HiveQL, and optimize data retrieval for different types of datasets.