附註:Includes bibliographical references and index.
Front Matter -- An Introduction: What's a Modern Big Data Platform -- A Bird's Eye View on Big Data -- A Minimal Data Processing and Management System -- Big Data Storage -- Offline Big Data Processing -- Stream Big Data Processing -- Data Analytics -- Data Science -- Data Discovery -- Data Security -- Putting All Together -- An Ideal Platform -- Appendix A: Further Systems and Patterns -- Appendix B: Recipes.
摘要:An array of tools are currently available for managing and processing data-some are ready-to-go solutions that can be immediately deployed, while others require complex and time-intensive setups. With such a vast range of options, choosing the right tool to build a solution can be complicated, as can determining which tools work well with each other. Designing Big Data Platforms provides clear and authoritative guidance on the critical decisions necessary for successfully deploying, operating, and maintaining Big Data systems. This highly practical guide helps readers understand how to process large amounts of data with well-known Linux tools and database solutions, use effective techniques to collect and manage data from multiple sources, transform data into meaningful business insights, and much more. Author Yusuf Aytas, a software engineer with a vast amount of big data experience, discusses the design of the ideal Big Data platform: one that meets the needs of data analysts, data engineers, data scientists, software engineers, and a spectrum of other stakeholders across an organization. Detailed yet accessible chapters cover key topics such as stream data processing, data analytics, data science, data discovery, and data security.
