rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Pro Hadoop Data Analytics
    sub title: Designing and Building Big Data Systems using the Hadoop Ecosystem
  name:
    name part: Koitzsch, Kerry.
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: xxu
    date issued: 2017
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XXI, 298 p. 161 illus., 152 illus. in color. online resource.
  abstract: 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. .
  table of contents: 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.
  note: by Kerry Koitzsch.
  subject:
    topic: Computer science
  subject:
    topic: Computer programming
  subject:
    topic: Programming languages (Electronic computers)
  subject:
    topic: Data mining
  subject:
    topic: Computer Science
  subject:
    topic: Big Data
  subject:
    topic: Programming Techniques
  subject:
    topic: Programming Languages, Compilers, Interpreters
  subject:
    topic: Data Mining and Knowledge Discovery
  related item:
    title info:
      title: Springer eBooks
  identifier: 9781484219102
  identifier: http://dx.doi.org/10.1007/978-1-4842-1910-2
  record info:
    record creation date: 161229
    record change date: 20161229141114.0
    record identifier: 978-1-4842-1910-2