The emergence of cloud computing and large-scale Internet services has given rise to new classes of data management systems, commonly referred to as NoSQL systems. The NoSQL systems provide high scalability and availability, however they provide only limited form of transaction support and weak consistency models. There are many applications that require more useful transaction and data consistency models than those currently provided by the NoSQL systems. In this thesis, we address the problem of providing scalable transaction support and appropriate consistency models for cluster based as well as geo-replicated NoSQL systems. The models we develop in this thesis are founded upon the snapshot isolation (SI) model which has been recognized as attractive for scalability. In supporting transactions on cluster-based NoSQL systems, we introduce a notion of decoupled transaction management in which transaction management functions are decoupled from storage system and integrated with the application layer. We present two system architectures based on this concept. In the first system architecture all transaction management functions are executed in a fully decentralized manner by the application processes. The second architecture is based on a hybrid approach in which the conflict detection functions are performed by a dedicated service. Because the SI model can lead to non-serializable transaction executions, we investigate two approaches for ensuring serializability. We perform a comparative evaluation of the two architectures and approaches for guaranteeing serializability and demonstrate their scalability. For transaction management in geo-replicated systems, we propose an SI based transaction model, referred to as causal snapshot isolation (CSI), which provides causal consistency using asynchronous replication. The causal consistency model provides more useful consistency guarantees than the eventual consistency model. We build upon the CSI model to provide an efficient transaction model for partially replicated databases, addressing the unique challenges raised due to partial replication in supporting snapshot isolation and causal consistency. Through experimental evaluations, we demonstrate the scalability and performance of our mechanisms.