Pro Hadoop Data Analytics

Designing and Building Big Data Systems using the Hadoop Ecosystem
1. Auflage 2016
ISBN: 978-1-4842-1909-6
Verlag: Apress

Buch, Englisch, 298 Seiten, Format (B × H): 178 mm x 254 mm, Gewicht: 6105 g

Learn advanced analytical techniques and leverage existing tool kits 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 that go beyond the basics of classification, clustering, and recommendation.

 emphasizes best practices to ensure coherent, efficient development. A complete example system will be developed using standard third-party components that consist of the tool kits, libraries, visualization and reporting code, as well as support glue to provide a working and extensible end-to-end system.

The book also highlights the importance of end-to-end, flexible, configurable, high-performance data pipeline systems with analytical components as well as appropriate visualization results. You'll discover the importance of mix-and-match or hybrid systems, using different analytical components in one application. This hybrid approach will be prominent in the examples.

What You'll Learn 

• Build big data analytic systems with the Hadoop ecosystem
• Use libraries, tool kits, and algorithms to make development easier and more effective
• Apply metrics to measure performance and efficiency of components and systems
• Connect to standard relational databases, noSQL data sources, and more
• Follow case studies with example components to create your own systems

Who This Book Is For
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.

Zielgruppe

Professional/practitioner

Autoren/Hrsg.

Koitzsch, Kerry

Fachgebiete

• Mathematik | Informatik EDV | Informatik Daten / Datenbanken NoSQL, Objektorientierte Datenbanken
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Verteilte Datenbanken

Inhaltsverzeichnis

[PART I: CONCEPTS]Chapter 1: Overview: Building Data Analytic Systems with HadoopIn this chapter we discuss what analytic systems using Hadoop are, why they are important, data sources which may be used, and applications which are --- and are not suitable for a distributed system approach using Hadoop.Subtopics:1. Introduction: The Need for Distributed Analysis2. How the Hadoop Ecosystem Implements Big Data Analysis3. A Survey of the Hadoop Ecosystem4. Architectures for Building5. SummaryChapter 2: Programming Languages: A Scala and Python RefresherThis chapter consists of a concise overview of the Scala and Python programming languages, and details why these languages are important ingredients of most modern Hadoop analytical systems. The chapter is primarily aimed at Java/C++ programmers who need a quick review/introduction to the Scala and Python programming languages.
Subtopics:1. Introduction to Bayesian Analysis2. The Problem of Credit Fraud and Possible Solutions3. Basic Applications of the Data Models4. Examples of Fraud Detection5. SummaryChapter 16: Searching for Oil: Geological Data Analysis with MahoutIn this chapter, we describe a system which uses geospatial data, ontologies, and other semantic web information to predict where geological resources, such as oil or bauxite (aluminum ore) might be found.Subtopics:1. Introduction to the Geospatial Data Arena2. Components and Architecture^3. Data Sources for Geospatial Data4. Basic Examples and Visualizations5. Extended Examples6. SummaryChapter 17: ‘Image as Big Data’ Systems: Some Case StudiesIn this chapter, we describe the use of ‘images as big data’ and how image data may be used in combination with the Hadoop ecosystem to provide information for a variety of systems.Subtopics:1. Introduction to the Image as Big Data Concept2. Components and Architecture3. Data Sources for Imagery and How to Use Them4. The Image as Big Data Pipeline5. Examples6. SummaryChapter 18: A Generic Data Pipeline Analytical System In this chapter, we detail and end-to-end analytical system using many of the techniques we discussed throughout the book to provide an evaluation system the user may extend and edit to create her own Hadoop data analysis system.Subtopics:1. Architecture and Description of Example System2. How to obtain and run the system3. Basic examples4. Extended Examples5. How to extend the system for custom applications6. SummaryChapter 19:  Conclusions and The Future of Big Data AnalysisIn this chapter we sum up what we have learned in the previous chapters and discuss some of the developing trends in big  data analysis including ‘incubator’ projects and ‘young’ projects for data analysis, and we speculate on what the future holds for big data analysis and the Hadoop ecosystem (it can only continue to grow)Subtopics:1. Conclusions: The Current state of Hadoop Data Analytics<2. Future Hadoop Analysis: Speculations

Über Autor(innen)

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.