Creation of column-oriented NoSQL databases automatically in Big Data environments and its impact on energy consumption

Abstract

This study investigates the automatic creation of column-oriented NoSQL databases in Big Data environments and their impact on energy consumption. Traditional row-oriented databases face limitations in handling large volumes of data, resulting in slower query response times and energy inefficiencies. In contrast, column-oriented NoSQL databases store data in columns, enabling efficient compression, retrieval, and query processing. Innovative techniques are employed to automatically create these databases, optimizing performance and minimizing manual intervention. Storing data in a columnar format reduces storage requirements and power consumption while improving data locality and reducing I/O operations. This study emphasizes the benefits of adopting column-oriented NoSQL databases, including improved performance, scalability, and energy efficiency in Big Data environments.