UDEMY_TAMING_BIG_DATA_WITH_MAPREDUCE_AND_HADOOP_TUTORIAL-kEISO

Section

Appz

Group

kEISO

Size

1,60 GB

Files

37

Date

2015-07-26

NFO

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  ▀▓████▀▓█▀ ▀▓▀                                               ▀▓  ▀█▓▀████▓▀
 █ ▀█▓▓██▄▄██    TITLE: Taming Big Data with MapReduce and Hadoop ██▄▄██▓▓█▀ █
 █▀ ▐█▒▓██░██                                                     ██░██▓▒█▌ ▀█
 █▀  █▓███▒█▌                                                     ▐█▒███▓█  ▀█
 █▀ ▐█▓███▓█░                                                     ░█▓███▓█▌ ▀█
 █▀ ▐██████▌     PUBLISHER..........: UDEMY                        ▐██████▌ ▀█
 █▀ ▀▀████▓      LINK...............: http://is.gd/62wltx           ▓████▀▀ ▀█
 ▌ ▓██▌▄ ▀▀      AUTHOR.............: Frank Kane                    ▀▀ ▄▐██▓ ▐
 ▌▐███░▒███▌     LEVEL..............: all level                    ▐███▒░███▌▐
 ▌ ████▓████     RUNTIME............: 5 hours                      ████▓████ ▐
 █ ▐▓█▀█████░    LANGUAGE...........: ENGLISH                     ░█████▀█▓▌ █
 █▀ ▀▄█▄▄▀▀█▓    RELEASE TYPE.......: RETAIL                      ▓█▀▀▄▄█▄▀ ▀█
 █▀▄ ▄▀███▓▄     RELEASE FORMAT.....: ISO                          ▄▓███▀▄ ▄▀█
 █▀█ ▐▓▄▀████    STORE DATE:........: 2015.07.21                  ████▀▄▓▌ █▀█
 █▀█ ▐█▌▓██▓     RELEASE DATE.......: 2015.07.26                   ▓██▓▐█▌ █▀█
 ▓▀█ ▓▌░██▓      ISO SIZE...........: 1,714,198,528                 ▓██░▐▓ █▀▓
 ▒▀█ █ ▓█▓       ISO CHECKSUM.......: 5F874876                       ▓█▓ █ █▀▒
 ░ █ ▌░██        DISKCOUNT..........: 35 * 50MB                       ██░▐ █ ░
   ▓█▌▓█▌        SAVED MONEY........: YOU DID!                          ▐█▓▐█▓
 ░ ▒█▐██         DISKNAME...........: keiso_utbigdwmh                  ██▌█▒ ░
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█▀  ▓░  ▄ █▄▌██ ▄ ▀ ▀█▌▐▓▀         TUTORIAL          ▀▓▌▐█▀ ▀ ▄ ██▐▄█ ▄  ░▓  ▀█
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 █▀█ ▄▓  ▄ ▐▌                                                     ▐▌ ▄  ▓▄ █▀█
 █▀█ █▓ ▄▓▄▀ ôBig data" analysis is a hot skill û and this     ▀▄▓▄ ▓█ █▀█
 █▀█ ▓▒  ▒  course will teach you two technologies fundamental       ▒  ▒▓ █▀█
 █▀█ ▒░  ░  to big data quickly: MapReduce and Hadoop. Ever          ░  ░▒ █▀█
 ▓▀█ ▒░  wonder how Google manages to analyze the entire                ░▒ █▀▓
 ▒▀█ ░▓  Internet on a continual basis? You'll learn those              ▓░ █▀▒
 ░ █  ▒  same techniques, using your own Windows system                 ▒  █ ░
   ▓  ░  right at home.                                                 ░  ▓
 ░ ▒  ░                                                                 ░  ▒ ░
   ░ Learn and master the art of framing data analysis problems as      ░
   ░  MapReduce problems through hands-on examples, and then scale      ░
   ░  them up to run on cloud computing services in this course.        ░
   ░                                                                    ░
   ░ * Learn the concepts of MapReduce                                  ░
   ░ * Run MapReduce jobs quickly using Python and MRJob                ░
   ░ * Translate complex analysis problems into multi-stage MapReduce   ░
   ░  jobs                                                              ░
   ░ * Scale up to larger data sets using Amazon's Elastic              ░
   ░  MapReduce service                                                 ░
   ░ * Understand how Hadoop distributes MapReduce across               ░
   ░  computing clusters                                                ░
   ░ * Learn about other Hadoop technologies, like Hive,                ░
   ░  Pig, and Spark                                                    ░
   ░                                                                    ░
   ░ By the end of this course, you'll be running code that analyzes    ░
   ░  gigabytes worth of information û in the cloud û in a matter   ░
   ░  of minutes.                                                       ░
   ░                                                                    ░
   ░ We'll have some fun along the way. You'll get warmed up with       ░
   ░  some simple examples of using MapReduce to analyze movie ratings  ░
   ░  data and text in a book. Once you've got the basics under your    ░
   ░  belt, we'll move to some more complex and interesting tasks.      ░
   ░  We'll use a million movie ratings to find movies                  ░
   ░  that are similar to each other, and you might even                ░
   ░  discover some new movies you might like in the                    ░
   ░  process! We'll analyze a social graph of                          ░
   ░  superheroes, and learn who the most ôpopular"                   ░
   ░  superhero is û and develop a system to find                     ░
   ░  ôdegrees of separation" between superheroes. Are                ░
   ░  all Marvel superheroes within a few degrees of being              ░
   ░  connected to The Incredible Hulk? You'll find the answer.         ░
   ░                                                                    ░
   ░ This course is very hands-on; you'll spend most of your time       ░
   ░  following along with the instructor as we write, analyze, and run ░
   ░  real code together û both on your own system, and in the        ░
   ░  cloud using Amazon's Elastic MapReduce service. Over 5 hours of   ░
   ░  video content is included, with over 10 real examples of          ░
   ░  increasing complexity you can run and study yourself. Move        ░
   ░  through them at your own pace, on your own                        ░
   ░  schedule. The course wraps up with an overview of                 ░
   ░  other Hadoop-based technologies, including Hive,                  ░
   ░  Pig, and the very hot Spark framework û complete                ░
   ░  with a working example in Spark.                                  ░
   ░                                                                    ░
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 █▀█ █ █ ▐▌▐██▐█▄ ▓██▐█▌██░   ░▌   ▄▀▐ ░ ▌▀▄   ▐░   ░██▐█▌██▓ ▄█▌██▌▐▌ █ █ █▀█
  ▀█ ▀████ ██▀████▐████▓▀   ▄▀▀▀▄ ▄▄▓▐ ░ ▌▓▄▄ ▄▀▀▀▄   ▀▓████▌████▀██ ████▀ █▀
   ▀█▄  ▀▀▀████▓▀▄█▓▀▀     ▐▌ ▀▓ ▌▀▀▀▀▄▀▄▀▀▀▀▐ ▓▀ ▐▌     ▀▀▓█▄▀▓████▀▀▀  ▄█▀
      ▀ ▄▄    ▀▀▀▀▀▀▀   ▄▌  ▀▄▄▄▀ ▀▀▀▄ ▀ ▄▀▀▀ ▀▄▄▄▀  ▐▄   ▀████▀▀ ▄▄ ▄▄ ▀
          ▀▀█▄▄▄▄▄▄▄▄▄████▄    ▄  ██▌▐ █ ▌▐██  ▄    ▄████▄▀        ▀▄▄
                ▀▀▀▀▀▀▀▀██▓▓░░  ▄  ▀ ▐ ▓ ▌ ▀  ▄  ░░▓▓██▀▀▄ H7/BREAK ▄▀
                         ▄▀ ▄ ▀▄  ▓ ▄▐ ▒ ▌▄ ▓  ▄▀ ▄ ▀▄    ▀▐▄    ▄▌▀
                        ▐▌ ▌ ▐ ▐▌ ░  ▐ ░ ▌  ░ ▐▌ ▌ ▐ ▐▌
                         ▀▄ ▀ ▄▀▄▄   █ ░ █   ▄▄▀▄ ▀ ▄▀
                           ▀▀▀ █  █  █ ░ █  █  █ ▀▀▀
                                ▀▀  ▐▐ ░ ▌▌  ▀▀
                                    █▐ ░ ▌█
                                   ▀▄▐ ░ ▌▄▀
                                     ▐ ▒ ▌
                                     ▐ ▓ ▌
                                    ▄▀ ■ ▀▄
                                   ▐▌ ■░■ ▐▌
                                    ▀▄ ▀ ▄▀
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