Pro Hadoop Data Analytics: Designing and Building Big Data Systems using the Hadoop Ecosystem - Tapa blanda

Acerca del autor

Kerry Koitzsch is a software engineer and interested in the early history of science, particularly chemistry. He frequently publishes papers and attends conferences on scientific and historical topics, including early chemistry and alchemy, and sociology of science. He has presented many lectures, talks, and demonstrations on a variety of subjects for the United States Army, the Society for Utopian Studies, American Association for Artificial Intelligence (AAAI), Association for Studies in Esotericism (ASE), and others. He has also published several papers and written two historical books.
Kerry was educated at Interlochen Arts Academy, MIT, and the San Francisco Conservatory of Music. He served in the United States Army and United States Army Reserve, and is the recipient of the United States Army Achievement Medal.  He has been a software engineer specializing in computer vision, machine learning, and database technologies for 30 years, and currently lives and works in Sunnyvale, California.

De la contraportada

Learn advanced analytical techniques and leverage existing toolkits to make your analytic applications more powerful, precise, and efficient. This book provides the right combination of architecture, design, and implementation information to create analytical systems which go beyond the basics of classification, clustering, and recommendation.

In Pro Hadoop Data Analytics best practices are emphasized to ensure coherent, efficient development. A complete example system will be developed using standard third-party components which will consist of the toolkits, libraries, visualization and reporting code, as well as support glue to provide a working and extensible end-to-end system.

• The importance of end-to-end, flexible, configurable, high-performance data pipeline systems with analytical components as well as appropriate visualization results. Deep-dive topics will include Spark, H20, Vopal Wabbit (NLP), Stanford  NLP, and other appropriate toolkits and plugins.
Best practices and structured design principles. This will include strategic topics as well as the how to example portions.
• The importance of mix-and-match or hybrid systems, using different analytical components in one application to accomplish application goals. The hybrid approach will be prominent in the examples.
• Use of existing third-party libraries is key to effective development. Deep dive examples of the functionality of some of these toolkits will be showcased as you develop the example system.