UDEMY_LEARN_HADOOP_MAPREDUCE_AND_BIGDATA_FROM_SCRATCH_TUTORIAL-kEISO

Section

Appz

Group

kEISO

Size

2,96 GB

Files

66

Date

2015-06-23

NFO

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  ▀▓████▀▓█▀ ▀▓▀                                               ▀▓  ▀█▓▀████▓▀
 █ ▀█▓▓██▄▄██    TITLE: Learn Hadoop, MapReduce and BigData from  ██▄▄██▓▓█▀ █
 █▀ ▐█▒▓██░██            Scratch                                  ██░██▓▒█▌ ▀█
 █▀  █▓███▒█▌                                                     ▐█▒███▓█  ▀█
 █▀ ▐█▓███▓█░                                                     ░█▓███▓█▌ ▀█
 █▀ ▐██████▌     PUBLISHER..........: UDEMY                        ▐██████▌ ▀█
 █▀ ▀▀████▓      LINK...............: http://is.gd/WHIXR3           ▓████▀▀ ▀█
 ▌ ▓██▌▄ ▀▀      AUTHOR.............: Eduonix Learning Solutions    ▀▀ ▄▐██▓ ▐
 ▌▐███░▒███▌     LEVEL..............: all level                    ▐███▒░███▌▐
 ▌ ████▓████     RUNTIME............: 16 hours                     ████▓████ ▐
 █ ▐▓█▀█████░    LANGUAGE...........: ENGLISH                     ░█████▀█▓▌ █
 █▀ ▀▄█▄▄▀▀█▓    RELEASE TYPE.......: RETAIL                      ▓█▀▀▄▄█▄▀ ▀█
 █▀▄ ▄▀███▓▄     RELEASE FORMAT.....: ISO                          ▄▓███▀▄ ▄▀█
 █▀█ ▐▓▄▀████    STORE DATE:........: 2014.05.18                  ████▀▄▓▌ █▀█
 █▀█ ▐█▌▓██▓     RELEASE DATE.......: 2015.06.23                   ▓██▓▐█▌ █▀█
 ▓▀█ ▓▌░██▓      ISO SIZE...........: 3,175,368,704                 ▓██░▐▓ █▀▓
 ▒▀█ █ ▓█▓       ISO CHECKSUM.......: 922F999B                       ▓█▓ █ █▀▒
 ░ █ ▌░██        DISKCOUNT..........: 64 * 50MB                       ██░▐ █ ░
   ▓█▌▓█▌        SAVED MONEY........: YOU DID!                          ▐█▓▐█▓
 ░ ▒█▐██         DISKNAME...........: keiso_ulhmbfs                    ██▌█▒ ░
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█▀  ▓░  ▄ █▄▌██ ▄ ▀ ▀█▌▐▓▀         TUTORIAL          ▀▓▌▐█▀ ▀ ▄ ██▐▄█ ▄  ░▓  ▀█
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 █▀█ ▄▓  ▄ ▐▌                                                     ▐▌ ▄  ▓▄ █▀█
 █▀█ █▓ ▄▓▄▀ Modern companies estimate that only 12% of their      ▀▄▓▄ ▓█ █▀█
 █▀█ ▓▒  ▒  accumulated data is analyzed, and IT professionals       ▒  ▒▓ █▀█
 █▀█ ▒░  ░  who are able to work with the remaining data are         ░  ░▒ █▀█
 ▓▀█ ▒░  becoming increasingly valuable to companies. Big               ░▒ █▀▓
 ▒▀█ ░▓  data talent requests are also up 40% in the past               ▓░ █▀▒
 ░ █  ▒  year.                                                          ▒  █ ░
   ▓  ░                                                                 ░  ▓
 ░ ▒  ░ Simply put, there is too much data and not enough professionals ░  ▒ ░
   ░  to manage and analyze it. This course aims to close the gap by    ░
   ░  covering MapReduce and its most popular implementation: Apache    ░
   ░  Hadoop. We will also cover Hadoop ecosystems and the practical    ░
   ░  concepts involved in handling very large data sets.               ░
   ░                                                                    ░
   ░ Learn and Master the Most Popular Big Data Technologies in this    ░
   ░  Comprehensive Course.                                             ░
   ░                                                                    ░
   ░ * Apache Hadoop and MapReduce on Amazon EMR                        ░
   ░ * Hadoop Distributed File System vs. Google File                   ░
   ░  System                                                            ░
   ░ * Data Types, Readers, Writers and Splitters                       ░
   ░ * Data Mining and Filtering                                        ░
   ░ * Shell Comments and HDFS                                          ░
   ░ * Cloudera, Hortonworks and Apache Bigtop Virtual                  ░
   ░  Machines                                                          ░
   ░                                                                    ░
   ░ Mastering Big Data for IT Professionals World Wide                 ░
   ░ Broken down, Hadoop is an implementation of the MapReduce          ░
   ░  Algorithm and the MapReduce Algorithm is used in Big Data to      ░
   ░  scale computations. The MapReduce algorithms load a block of data ░
   ░  into RAM, perform some calculations, load the next block, and     ░
   ░  then keep going until all of the data has been processed from     ░
   ░  unstructured data into structured data.                           ░
   ░                                                                    ░
   ░ IT managers and Big Data professionals who know how                ░
   ░  to program in Java, are familiar with Linux, have                 ░
   ░  access to an Amazon EMR account, and have Oracle                  ░
   ░  Virtualbox or VMware working will be able to access               ░
   ░  the key lessons and concepts in this course and                   ░
   ░  learn to write Hadoop jobs and MapReduce programs.                ░
   ░                                                                    ░
   ░ This course is perfect for any data-focused IT job that seeks to   ░
   ░  learn new ways to work with large amounts of data.                ░
   ░                                                                    ░
   ░ Contents and Overview                                              ░
   ░ In over 16 hours of content including 74 lectures, this course     ░
   ░  covers necessary Big Data terminology and the use of Hadoop       ░
   ░  and MapReduce.                                                    ░
   ░                                                                    ░
   ░ This course covers the importance of Big Data, how to              ░
   ░  setup Node Hadoop pseudo clusters, work with the                  ░
   ░  architecture of clusters, run multi-node clusters on              ░
   ░  Amazons EMR, work with distributed file systems                   ░
   ░  and operations including running Hadoop on                        ░
   ░  HortonWorks Sandbox and Cloudera.                                 ░
   ░                                                                    ░
   ░ Students will also learn advanced Hadoop development, MapReduce    ░
   ░  concepts, using MapReduce with Hive and Pig, and know the Hadoop  ░
   ░  ecosystem among other important lessons.                          ░
   ░                                                                    ░
   ░ Upon completion students will be literate in Big Data              ░
   ░  terminology, understand how Hadoop can be used to overcome        ░
   ░  challenging Big Data scenarios, be able to analyze and            ░
   ░  implement MapReduce workflow, and be able to use virtual machines ░
   ░  for code and development testing and configuring                  ░
   ░  jobs.                                                             ░
   ░                                                                    ░
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 ▌ ░▓█                                                                   █▓░ ▐
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 █▀█ █  ▀█▄▀█▄▀█▄▀▀█▄   █▓  ▀█ ▄ ▄ ▀ ▐ ▓ ▌ ▀ ▄ ▄ █▀  ▓█   ▄█▀▀▄█▀▄█▀▄█▀  █ █▀█
 █▀█ █ █  ▀█▄▀█ ▀██▄▀█▄ ▐█▓░  ▀ ■    ▐ ▒ ▌    ■ ▀  ░▓█▌ ▄█▀▄██▀ █▀▄█▀  █ █ █▀█
 █▀█ █ █ ▐▌▐██▐█▄ ▓██▐█▌██░   ░▌   ▄▀▐ ░ ▌▀▄   ▐░   ░██▐█▌██▓ ▄█▌██▌▐▌ █ █ █▀█
  ▀█ ▀████ ██▀████▐████▓▀   ▄▀▀▀▄ ▄▄▓▐ ░ ▌▓▄▄ ▄▀▀▀▄   ▀▓████▌████▀██ ████▀ █▀
   ▀█▄  ▀▀▀████▓▀▄█▓▀▀     ▐▌ ▀▓ ▌▀▀▀▀▄▀▄▀▀▀▀▐ ▓▀ ▐▌     ▀▀▓█▄▀▓████▀▀▀  ▄█▀
      ▀ ▄▄    ▀▀▀▀▀▀▀   ▄▌  ▀▄▄▄▀ ▀▀▀▄ ▀ ▄▀▀▀ ▀▄▄▄▀  ▐▄   ▀████▀▀ ▄▄ ▄▄ ▀
          ▀▀█▄▄▄▄▄▄▄▄▄████▄    ▄  ██▌▐ █ ▌▐██  ▄    ▄████▄▀        ▀▄▄
                ▀▀▀▀▀▀▀▀██▓▓░░  ▄  ▀ ▐ ▓ ▌ ▀  ▄  ░░▓▓██▀▀▄ H7/BREAK ▄▀
                         ▄▀ ▄ ▀▄  ▓ ▄▐ ▒ ▌▄ ▓  ▄▀ ▄ ▀▄    ▀▐▄    ▄▌▀
                        ▐▌ ▌ ▐ ▐▌ ░  ▐ ░ ▌  ░ ▐▌ ▌ ▐ ▐▌
                         ▀▄ ▀ ▄▀▄▄   █ ░ █   ▄▄▀▄ ▀ ▄▀
                           ▀▀▀ █  █  █ ░ █  █  █ ▀▀▀
                                ▀▀  ▐▐ ░ ▌▌  ▀▀
                                    █▐ ░ ▌█
                                   ▀▄▐ ░ ▌▄▀
                                     ▐ ▒ ▌
                                     ▐ ▓ ▌
                                    ▄▀ ■ ▀▄
                                   ▐▌ ■░■ ▐▌
                                    ▀▄ ▀ ▄▀
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