Pro Hadoop Data Analytics: Designing and Building big Data Systems Using the Hadoop Ecosystem (en Inglés)
Reseña del libro "Pro Hadoop Data Analytics: Designing and Building big Data Systems Using the Hadoop Ecosystem (en Inglés)"
Long Description:Effective data analytics, particularly when the data is complex, high-volume, or unstructured, is particularly challenging. Distributed solutions have recently becomeLong Description:Effective data analytics, particularly when the data is complex, high-volume, or unstructured, is particularly challenging. Distributed solutions have recently become available but the ability to build end-to-end analytical systems using Hadoop and its ecosystem have remained extremely challenging. In this book, advanced analytical techniques are described, implemented, and showcased as examples to provide a means for advanced programmers to leverage existing toolkits to make their analytic applications more powerful, precise, and efficient. Pro Hadoop Data Analytics by Kerry Koitzsch will provide the right combination of architecture, design, and implementation information to create analytical systems which go beyond the basics of classification, clustering, and recommendation. Best practices will be emphasized throughout the technology descriptions in order to insure coherent, efficient development. A git contribution will be provided as an end-to-end example of the techniques described in the book. Th e book emphasizes on four important topics throughout the book: 1. 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. 2. Best practices and struc tured design principles will be emphasized throughout the ?deep dive? topics. This will include strategic topics as well as the ?how to? example portions. 3. The importance of ?mix and match? or ?hybrid? systems to accomplish application goals. The ?hybrid? approach will be prominent in the example ?deep dives? 4. 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 we develop the example system. A complete example system will be developed using standard third-party components, to be submitted to git, which will consist of the toolkits, libraries, visualization and reporting code, and support ?glue? to provide a working and extensible end-to-end system. How to obtain the example code, run, and evaluate the code will be provided as an appendix or in another suitable location. An integrated system, particularly the ontology, NLP, and ?image as big data? components, currently does not exist, so in this sense a contribution and associated book would be a game-changer.Readership (who?s the target audience?):Software engineers, architects, and data scientists with an interest in the design and implementation of big data analytical systems using Hadoop , the Hadoop ecosystem, and other associated technologies.What you will learn: (Please give us a bulleted list of 5 or fewer items)1. The what, why, and how of building big data analytic systems with the Hadoop ecosystem 2. Libraries, toolkits, and algorithms to make development easier and more effective 3. Best practices to use when building analytic systems with Hadoop, and metrics to measure performance and efficiency of components and systems 4. How to connect to standard relational databases, noSQL data sources, and more 5. Useful case studies and example components which assist you in creating your own systems.
