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The world is awash with digital data from social networks, blogs, business, science and engineering. Data-intensive computing facilitates understanding of complex problems that must process massive amounts of data. Through the development of new classes of software, algorithms and hardware, data-intensive applications can provide timely and meaningful analytical results in response to exponentially growing data complexity and associated analysis requirements. This emerging area brings many challenges that are different from traditional high-performance computing. This reference for computing professionals and researchers describes the dimensions of the field, the key challenges, the state of the art and the characteristics of likely approaches that future data-intensive problems will require. Chapters cover general principles and methods for designing such systems and for managing and analyzing the big data sets of today that live in the cloud and describe example applications in bioinformatics and cybersecurity that illustrate these principles in practice. Table of Contents 1. Data-intensive computing: a challenge for the twenty-first century 2. The anatomy of data-intensive computing applications 3. Hardware architectures for data-intensive computing problems: a case study for string matching 4. Data management architectures 5. Large-scale data management techniques in cloud computing platforms 6. Dimension reduction for streaming data 7. Binary classification with support vector machines 8. Beyond MapReduce: new requirements for scalable data processing 9. Letting the data do the talking: hypothesis discovery from large-scale data sets in real time 10. Data-intensive visual analysis for cybersecurity.