This course builds on skills gained in database management systems and gives students an in-depth understanding of a wide range of fundamental Big Data Management systems. In particular, this course focuses on the “variety” of the 3Vs in big data, where how to store, index and query various types of data (structured, unstructured, geo-spatial and time series data) in a real-world application. Moreover, this courses introduce an end-to-end infrastructure to solve big data management problems, which include data cleaning, data integration, data update, query processing (top-k query, k-nearest neighbour query, range query, point query), data visualization, data crowdsourcing, from front-end to back-end. The students are expected to establish the skills to extract core efficiency/scalability challenges from a real-life application scenario, in order to identify and address the bottleneck of a big data management system. This course establishes a strong working knowledge of the concepts, techniques and products associated with Big Data. The main focus is on specialized storage models, indexing techniques, efficient and scalable algorithm designs for query processing, to work with a variety of Big Data. Students will learn the core functionality of each major Big Data component and how they integrate to form a coherent solution with business benefit. Hands-on programming and algorithm design exercises aim to provide insight into what the tools do so that their role in Big Data systems can be understood. Beyond the theory part, this course tries to bridge the theory to the application. In particular, it will introduce how to explore the usability of heterogeneous data, like how Google Maps work on spatial-textual data, how to analyse the user activity trajectory data for route planning, capacity estimation in intelligent transportation, how to do data integration and cleaning before data analytics, how to do data crowdsourcing for tasks to combine the expertise of computer and human being.