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  • 1
    Online Resource
    Online Resource
    MapReduce design patterns (2012)
    Beijing ; : O'Reilly
    Details
    Language: English
    Pages: 1 online resource (1 v.) , ill.
    Parallel Title: Erscheint auch als
    Keywords: Apache Hadoop (Computer file) ; MapReduce (Computer program) ; Electronic data processing ; Distributed processing ; Cluster analysis ; Data processing ; Software patterns ; Computer algorithms ; Electronic books ; Electronic books ; local
    Abstract: Until now, design patterns for the MapReduce framework have been scattered among various research papers, blogs, and books. This handy guide brings together a unique collection of valuable MapReduce patterns that will save you time and effort regardless of the domain, language, or development framework you're using. Each pattern is explained in context, with pitfalls and caveats clearly identified to help you avoid common design mistakes when modeling your big data architecture. This book also provides a complete overview of MapReduce that explains its origins and implementations, and why design patterns are so important. All code examples are written for Hadoop. Summarization patterns: get a top-level view by summarizing and grouping data Filtering patterns: view data subsets such as records generated from one user Data organization patterns: reorganize data to work with other systems, or to make MapReduce analysis easier Join patterns: analyze different datasets together to discover interesting relationships Metapatterns: piece together several patterns to solve multi-stage problems, or to perform several analytics in the same job Input and output patterns: customize the way you use Hadoop to load or store data "A clear exposition of MapReduce programs for common data processing patterns-this book is indespensible for anyone using Hadoop." --Tom White, author of Hadoop: The Definitive Guide
    Note: "Building effective algorithms and analytics for Hadoop and other systems"--Cover. - Includes index. - Description based on print version record
    URL: https://learning.oreilly.com/library/view/-/9781449341954/?ar
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    MPI Ethno. Forsch.
  • 2
    Online Resource
    Online Resource
    An introduction to Apache Accumulo : how it works, why it exists, and how it is used (2014)
    [Place of publication not identified] : O'Reilly Media
    Details
    Language: English
    Pages: 1 streaming video file (45 min., 36 sec.) , digital, sound, color.
    Keywords: Apache (Computer file : Apache Group) ; Data encryption (Computer science) ; Application software ; Programming ; Electronic videos ; local
    Abstract: "This webcast will cover the basics of Apache Accumulo architecture and how it works, along with examples of how it is used. We'll also talk about some interesting use cases, such as text indexing, fine-grained multi-level access controls, and storing large-scale graphs. We'll also briefly touch on what sets Accumulo apart from other similar and not-so similar systems and where we think the Accumulo project is headed in a technical direction."--Resource description page.
    Note: Title from title screen (viewed November 17, 2014). - Imprint from resource description page
    URL: lizenzpflichtig
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    Online Resource
    MPI Ethno. Forsch.
  • 3
    Online Resource
    Online Resource
    Hadoop with Python (2015)
    Sebastopol, CA : O'Reilly Media
    Details
    ISBN: 9781491942277 , 1491942274
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Edition: First edition.
    Keywords: Apache Hadoop ; Python (Computer program language) ; Electronic books ; Electronic books ; local
    Abstract: Hadoop is mostly written in Java, but that doesn't exclude the use of other programming languages with this distributed storage and processing framework, particularly Python. With this concise book, you'll learn how to use Python with the Hadoop Distributed File System (HDFS), MapReduce, the Apache Pig platform and Pig Latin script, and the Apache Spark cluster-computing framework. Authors Zachary Radtka and Donald Miner from the data science firm Miner & Kasch take you through the basic concepts behind Hadoop, MapReduce, Pig, and Spark. Then, through multiple examples and use cases, you'll learn how to work with these technologies by applying various Python tools. Use the Python library Snakebite to access HDFS programmatically from within Python applications Write MapReduce jobs in Python with mrjob, the Python MapReduce library Extend Pig Latin with user-defined functions (UDFs) in Python Use the Spark Python API (PySpark) to write Spark programs with Python Learn how to use the Luigi Python workflow scheduler to manage MapReduce jobs and Pig scripts Zachary Radtka, a platform engineer at Miner & Kasch, has extensive experience creating custom analytics that run on petabyte-scale data sets.
    Note: Description based on online resource; title from title page (viewed January 3, 2019)
    URL: https://learning.oreilly.com/library/view/-/9781492048435/?ar
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  • 4
    Online Resource
    Online Resource
    Hadoop : What You Need to Know (2016)
    [Erscheinungsort nicht ermittelbar] : O'Reilly Media, Inc. | Boston, MA : Safari
    Details
    Language: English
    Pages: 1 online resource (39 pages)
    Edition: 1st edition
    Keywords: Electronic books ; local
    Abstract: Hadoop has revolutionized data processing and enterprise data warehousing, but its explosive growth has come with a large amount of uncertainty, hype, and confusion. With this report, enterprise decision makers will receive a concise crash course on what Hadoop is and why it’s important. Hadoop represents a major shift from traditional enterprise data warehousing and data analytics, and its technology can be daunting at first. Donald Miner, founder of the data science firm Miner & Kasch, covers just enough ground so you can make intelligent decisions about Hadoop in your enterprise. By the end of this report, you’ll know the basics of technologies such as HDFS, MapReduce, and YARN, without becoming mired in the details. Not only will you learn the basics of how Hadoop works and why it’s such an important technology, you’ll get examples of how you should probably be using it.
    Note: Online resource; Title from title page (viewed March 15, 2016)
    URL: https://learning.oreilly.com/library/view/-/9781492042372/?ar
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