UDEMI_Taming Big Data with MapReduce and Hadoop

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Added on August 21, 2015 by VarioMario2in Books > Ebooks
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UDEMI_Taming Big Data with MapReduce and Hadoop (Size: 3.19 GB)
 keiso_utbigdwmh.iso1.6 GB
 1_-_Introduction.mp432.59 MB
 2_-_How_to_Use_This_Course.mp431.08 MB
 3.1_-_Enthought_Canopy_website.txt42 bytes
 3_-_Installing_Enthought_Canopy.mp448.41 MB
 4_-_Installing_MRJob.mp420.67 MB
 5_-_Downloading_the_MovieLens_Data_Set.mp426.58 MB
 6_-_Run_Your_First_MapReduce_Job.mp438.39 MB
 10_-_Average_Friends_by_Age_Example_-_Part_1.mp412.13 MB
 11.1_-_Friends_By_Age_.py469 bytes
 11.2_-_Data_for_FriendsByAge.py.txt48 bytes
 11_-_Average_Friends_by_Age_Example_-_Part_2.mp434.24 MB
 12.1_-_Min_Temperatures_.py597 bytes
 12_-_Minimum_Temperature_By_Location_Example.mp439.86 MB
 13.1_-_Max_Temperatures_.py605 bytes
 13.2_-_Temperature_data_file.txt41 bytes
 13_-_Maximum_Temperature_By_Location_Example.mp418.46 MB
 14.1_-_Word_Frequency_.py331 bytes
 22.1_-_Most_Popular_Movie_.py683 bytes
 22_-_Example_-_Most_Popular_Movie.mp428.22 MB
 23.1_-_Most_Popular_Movie_Nicer_.py1.16 KB
 23_-_Including_Ancillary_Lookup_Data_in_the_Example.mp445.39 MB
 24_-_Example_-_Most_Popular_Superhero_Part_1.mp417.13 MB
 25.1_-_Marvel_Graph.txt1.6 MB
 25.2_-_Marvel_Names.txt343.6 KB
 25.3_-_Most_Popular_Superhero_.py1.46 KB
 25_-_Example_-_Most_Popular_Superhero_Part_2.mp432.33 MB
 26_-_Example_-_Degrees_of_Separation_-_Concepts.mp432.54 MB
 34.1_-_Hadoop_website.txt26 bytes
 34_-_Fundamental_Concepts_of_Hadoop.mp430.74 MB
 35_-_The_Hadoop_Distributed_File_System_HDFS_.mp410.6 MB
 36.1_-_YARN_website.txt76 bytes
 36_-_Apache_YARN.mp420.23 MB
 37_-_Hadoop_Streaming_-_How_Hadoop_Runs_your_Python_Code.mp420.35 MB
 38.1_-_Amazon_Web_Services_Homepage.txt22 bytes
 38_-_Setting_Up_Your_Amazon_Elastic_MapReduce_Account.mp431.61 MB
 39_-_Linking_Your_EMR_Account_with_MRJob.mp414 MB
 40_-_Exercise_-_Run_Movie_Recommendations_on_Elastic_MapReduce.mp425.95 MB
 41_-_Analyze_the_Results_of_Your_EMR_Job.mp425.15 MB
 42_-_Distributed_Computing_Fundamentals.mp416.67 MB
 43.1_-_Movie_Similarities_.py4.41 KB
 43_-_Activity_-_Running_Movie_Similarities_on_Four_Machines.mp426.41 MB
 44_-_Analyzing_the_Results_of_the_4-Machine_Job.mp465.9 MB
 45_-_Troubleshooting_Hadoop_Jobs_with_EMR_and_MRJob_Part_1.mp423.31 MB
 46.1_-_MRJob_EMR_Documentation.txt57 bytes
 46_-_Troubleshooting_Hadoop_Jobs_Part_2.mp461.76 MB
 47_-_Analyzing_One_Million_Movie_Ratings_Across_16_Machines_Part_1.mp432.05 MB
 48.1_-_Movie_Similarities_Large_.py4.33 KB
 48_-_Analyzing_One_Million_Movie_Ratings_Across_16_Machines_Part_2.mp452.27 MB
 49.1_-_Hive_Website.txt24 bytes
 49_-_Introducing_Apache_Hive.mp415.62 MB
 50.1_-_Pig_Website.txt23 bytes
 50_-_Introducing_Apache_Pig.mp422.74 MB
 51.1_-_Spark_website.txt24 bytes
 51_-_Apache_Spark_-_Concepts.mp431.19 MB
 52.1_-_AWS_Spark_on_EMR_blog_entry.txt64 bytes
 52.2_-_Scala_source_code_for_the_flights_example.txt52 bytes
 52_-_Spark_Example_-_Part_1.mp464.56 MB
 53_-_Spark_Example_-_Part_2.mp417.22 MB
 54_-_Congratulations_.mp413.21 MB
 All Files - tamingBigdata0 bytes

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UDEMI Taming Big Data with MapReduce and Hadoop

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3.19 GB
seeders:1
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UDEMI_Taming Big Data with MapReduce and Hadoop