Big Data is a concept of major relevance in today''s world, sometimes highlighted as a key asset for productivity growth, innovation, and customer relationship, whose popularity has increased considerably during the last years. Areas like smart cities , manufacturing, retail, finance, software development, environment, digital media, among others, can benefit from the collection, storage, processing, and analysis of Big Data, leveraging unprecedented data-driven workflows and considerably improved decision-making processes. This new type of data is being defined not only by its characteristics (e.g., volume, variety, and velocity), but also by the limitations it imposes on traditional storage and processing technologies. Organizations seeking Big Data initiatives face several challenges, such as the lack of consensus in definitions, models, and architectures, as well as difficulties concerning the Big Data life cycle design and implementation. The concept of Big Data Warehouse is emerg ing as either an augmentation or a replacement of the traditional Data Warehouse, a concept that has a long history as one of the most valuable enterprise data assets. Nevertheless, research in Big Data Warehousing is still in its infancy, lacking an integrated and validated approach for designing and implementing both the logical layer (data models, data flows, and interoperability between components) and the physical layer (technological infrastructure) of these complex systems. This book ad dresses models and methods for designing and implementing Big Data Systems to support mixed and complex decision processes, giving special attention to Big Data Warehouses as a way of efficiently storing and processing batch or streaming data for