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Overview of Patterns - Intro to Hadoop and MapReduce

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    So, in this lesson, what patterns are we going to learn? Well, we'll learn about
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    filtering patterns, we'll learn about summarization patterns,
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    and something we'll call structural patterns. And
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    these are all just frameworks for using
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    MapReduce for solve problems that you've probably
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    encountered as a programmer before. Filtering patterns
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    solve filtering problems. Like, sampling data, or,
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    maybe even generating some top-n list. Maybe,
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    top ten something. Summarization patterns solve summarization problems. Getting
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    a high-level view of your data. Things like counting records. Finding
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    min and max, then calculating basic statistical values, like
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    mean, median, and mode. We can also use these patterns to create
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    an index, finally, these structural patterns will solves structural
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    problems. And, the one more focus on, in this
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    lesson, is combining two data sets. And, just to
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    warn you, this lesson will go fast, and, that
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    means you probably won't remember everything about all of
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    these patterns. Totally okay, not expected. The point of
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    this lesson, is for you to know that these
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    patterns exist. And that if you ever find yourself
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    in a situation with big data where you say this problem is really just a
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    filtering or a summarization problem. You'll know where
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    to look, to find a technique to solve.
タイトル:
Overview of Patterns - Intro to Hadoop and MapReduce
概説:

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Video Language:
English
Team:
Udacity
プロジェクト:
ud617 - Intro to Hadoop and Mapreduce
Duration:
01:27

English subtitles

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