Donald Miner

Donald Miner

Washington DC-Baltimore Area
2K followers 500+ connections

About

Don is founder and CEO of Space Cow Media, a company building tools for the Magic: The…

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Experience

  • Space Cow Media Graphic

    Space Cow Media

    Baltimore, Maryland, United States

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    Baltimore, Maryland Area

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    Maryland

Education

  • University of Maryland Baltimore County Graphic

    University of Maryland Baltimore County

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    My dissertation research focused on machine learning and multi-agent systems. I built an approach for using machine learning models to predict and control the behavior of previously hard to control swarms of agents. The research has applications in massively distributed edge computing systems.

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Publications

  • Hadoop with Python

    O'Reilly Media

    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…

    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.

    Other authors
    • Zachary Radtka
    See publication
  • MapReduce Design Patterns

    O'Reilly Media

    Design patterns for the MapReduce framework, until now, 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—so you can avoid some of the common design mistakes when modeling your Big Data…

    Design patterns for the MapReduce framework, until now, 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—so you can avoid some of the 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.

    Hadoop MapReduce code is provided to help you learn how to apply the design patterns by example.

    Topics include:
    - Basic patterns, including map-only filter, group by, aggregation, distinct, and limit
    - Joins: traditional reduce-side join, reduce-side join with Bloom filter, replicated join with distributed cache, merge join, Cartesian products, and intersections
    - Binning, sharding for other systems, sorting, sampling, unions, and other patterns for organizing data
    - Job optimization patterns, including multi-job map-only job folding, and overloading the key grouping to perform two jobs at once

    Other authors
    See publication

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