International Journal of Science and Technology (IJST)

Integrated Strategies for Efficient Data Management in NoSQL Databases

As data volumes continue to grow at an exponential rate, managing and storing this data efficiently has become increasingly crucial. NoSQL databases, with their ability to handle unstructured data, have emerged as a preferred solution, offering superior scalability and flexibility compared to traditional relational database management systems (RDBMS). Techniques such as deduplication (eliminating duplicate data) and data compression, when combined with powerful tools like Hadoop and MongoDB, help optimize storage and reduce network usage. However, a key challenge in both NoSQL and SQL systems is managing schema evolution—adapting the database schema as application requirements change. This process introduces risks of downtime, data inconsistency, and disruptions to ongoing operations. The lack of a standardized approach to schema evolution often leads to inefficient management, particularly as systems scale. This paper reviews current methods used to address these challenges and highlights gaps in existing solutions. While some strategies attempt to automate schema changes or handle schema migrations in a way that reduces disruption, they often fall short in ensuring seamless transitions and maintaining data integrity. The paper proposes an integrated approach to schema management that combines the strengths of both NoSQL and SQL systems. By enhancing schema migration processes and focusing on consistency and downtime reduction, this approach aims to improve overall data management, providing a more reliable and efficient solution across diverse database environments.