Data drives knowledge which engenders innovation. Be it personalizing search results, recommending movies or friends, determining which advertisements to display or which coupon to deliver, data is central in improving customer satisfaction and providing a competitive edge. Data, therefore, generates wealth and many modern enterprises are collecting data at the most detailed level possible, resulting in massive and ever-growing data repositories. Such massive scale of data pose a number of research challenges, called big data challenges, which form the basis for my research. My research philosophy is to build database management systems (DBMSs) designed for large scale operations that expose abstractions to simplify application design while providing tools to ease system deployment and management. Using this philosophy as the cornerstone, my research has spanned the broad area of big data management encompassing both transaction processing and analytical processing systems exercising a synergy of both theoretical and practical system-oriented research. 1 Dissertation Research: Scalable, Elastic, and Autonomic OLTP Databases DBMSs serving mission critical user facing web-applications must be scalable, fault-tolerant, and highly available to serve the growing number of users and handle the increasing amounts of data. Classical relational DBMSs (RDBMSs) support generic transactions but are expensive to scale-out to large clusters. Key-value stores can scale-out but provide transactional access to only single key-value pairs, thereby considerably increasing the complexity of application