Scalable Data Partitioning Techniques for Distributed Data Processing in Cloud Environments

Cloud storage gives consumers easy, on-demand access to top-notch cloud apps while allowing them to save and retrieve data remotely. This ensures effective data storage on cloud servers while doing away with the need to manage local hardware and software. Users can gain access to nearly limitless computation and storage capabilities by utilizing cloud computing to get around resource limitations like memory and storage restrictions. The necessity of scalable data- partitioning strategies in remote cloud systems is underscored by the increasing dependence on cloud platforms for data- intensive operations, including real-time processing and data analytics. This study examines a number of data-partitioning strategies meant to improve scalability, optimize load distribution, and maximize system efficiency. The study intends to enhance cloud-based data processing procedures by investigating these approaches, allowing businesses to optimize cloud computing's potential for data-driven projects.