Pro Hadoop Data Analytics Designing and Building Big Data Systems using the Hadoop Ecosystem

Description

Chapter 1: Overview: Building Data Analytic Systems with Hadoop -- Chapter 2: A Scala and Python Refresher -- Chapter 3: Standard Toolkits for Hadoop and Analytics -- Chapter 4: Relational, noSQL, and Graph Databases -- Chapter 5: Data Pipelines and How to Construct Them -- Chapter 6: Advanced Search Techniques with Hadoop, Lucene, and Solr -- Chapter 7: An Overview of Analytical Techniques and Algorithms -- Chapter 8: Rule Engines, System Control, and System Orchestration -- Chapter 9: Putting it All Together: Designing a Complete Analytical System -- Chapter 10: Data Visualizers: Seeing and Interacting with the Analysis -- Chapter 11: A Case Study in Bioinformatics: Analyzing Microscope Slide Data -- Chapter 12: A Bayesian Analysis Software Component: Identifying Credit Card Fraud -- Chapter 13: Searching for Oil: Geological Data Analysis with Mahout -- Chapter 14: 'Image as Big Data' Systems: Some Case Studies -- Chapter 15: A Generic Data Pipeline Analytical System -- Chapter 16: Conclusions and The Future of Big Data Analysis.;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 book emphasizes four important topics: 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. . Chapter 1: Overview: Building Data Analytic Systems with Hadoop -- Chapter 2: A Scala and Python Refresher -- Chapter 3: Standard Toolkits for Hadoop and Analytics -- Chapter 4: Relational, noSQL, and Graph Databases -- Chapter 5: Data Pipelines and How to Construct Them -- Chapter 6: Advanced Search Techniques with Hadoop, Lucene, and Solr -- Chapter 7: An Overview of Analytical Techniques and Algorithms -- Chapter 8: Rule Engines, System Control, and System Orchestration -- Chapter 9: Putting it All Together: Designing a Complete Analytical System -- Chapter 10: Data Visualizers: Seeing and Interacting with the Analysis -- Chapter 11: A Case Study in Bioinformatics: Analyzing Microscope Slide Data -- Chapter 12: A Bayesian Analysis Software Component: Identifying Credit Card Fraud -- Chapter 13: Searching for Oil: Geological Data Analysis with Mahout -- Chapter 14: 'Image as Big Data' Systems: Some Case Studies -- Chapter 15: A Generic Data Pipeline Analytical System -- Chapter 16: Conclusions and The Future of Big Data Analysis.