A Review Of Data Management Techniques In Cloud Systems

Cloud computing has transformed the way organizations store, manage, and process large volumes of data by providing scalable, flexible, and cost-effective data management solutions. This review examines various data management techniques used in cloud systems, focusing on storage architectures, data processing frameworks, consistency models, and distributed database systems. It explores how cloud platforms handle structured, semi-structured, and unstructured data using technologies such as object storage, NoSQL databases, and data lakes. The study also discusses key data management approaches including data replication, partitioning, indexing, and caching to ensure performance, availability, and fault tolerance. Furthermore, it highlights the role of big data frameworks such as Hadoop and Spark in enabling large-scale data processing in cloud environments. The review identifies critical challenges such as data security, privacy, latency, scalability, and consistency issues in distributed systems. Emerging solutions like edge computing, hybrid cloud architectures, and AI-driven data optimization are also analyzed. The findings emphasize that efficient data management techniques are essential for maximizing the performance and reliability of modern cloud systems.