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[] [MANNING] The Ultimate Introduction to Big Data [FCO]

  • 收录时间:2020-02-17 02:28:14
  • 文件大小:5GB
  • 下载次数:66
  • 最近下载:2021-01-13 03:26:08
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文件列表

  1. 01 - Introduction, and install Hadoop on your desktop!.mp4 236MB
  2. 02 - Hadoop overview and history.mp4 108MB
  3. 39 - Why NoSQL.mp4 96MB
  4. 44 - Installing Cassandra.mp4 96MB
  5. 24 - Find the movie with the lowest average rating with RDDs.mp4 95MB
  6. 49 - Choosing a database technology.mp4 91MB
  7. 03 - Overview of the Hadoop ecosystem.mp4 91MB
  8. 27 - Movie recommendations with MLLib.mp4 90MB
  9. 80 - Analyze web logs published with Flume using Spark Streaming.mp4 89MB
  10. 84 - Count words with Storm.mp4 87MB
  11. 58 - Install Presto and query Hive with it.mp4 85MB
  12. 47 - Install MongoDB and integrate it with Spark.mp4 85MB
  13. 67 - Set up a simple Oozie workflow.mp4 82MB
  14. 56 - Integrate Phoenix with Pig.mp4 80MB
  15. 75 - Publishing web logs with Kafka.mp4 78MB
  16. 45 - Write Spark output into Cassandra.mp4 75MB
  17. 86 - Counting words with Flink.mp4 74MB
  18. 59 - Query both Cassandra and Hive using Presto.mp4 70MB
  19. 78 - Set up Flume to monitor a directory and store its data in HDFS.mp4 68MB
  20. 46 - MongoDB overview.mp4 66MB
  21. 26 - Find the movie with the lowest average rating wth DataFrames.mp4 65MB
  22. 42 - Use HBase with Pig to import data at scale.mp4 64MB
  23. 52 - Setting up Drill.mp4 64MB
  24. 41 - Import movie ratings into HBase.mp4 61MB
  25. 17 - Find the oldest movie with a 5-star rating using Pig.mp4 61MB
  26. 43 - Cassandra overview.mp4 61MB
  27. 94 - Books and online resources.mp4 61MB
  28. 29 - Check your results against mine!.mp4 59MB
  29. 79 - Spark Streaming - introduction.mp4 56MB
  30. 65 - Simulating a failing master with ZooKeeper.mp4 53MB
  31. 07 - Install the MovieLens dataset into HDFS using the command line.mp4 52MB
  32. 48 - Using the MongoDB shell.mp4 52MB
  33. 18 - Find old 5-star movies with Pig.mp4 52MB
  34. 81 - Exercise - Monitor Flume-published logs for errors in real time.mp4 52MB
  35. 14 - Check your results against mine!.mp4 49MB
  36. 05 - HDFS - what it is and how it works.mp4 49MB
  37. 62 - Use Hive on Tez and measure the performance benefit.mp4 48MB
  38. 36 - Install MySQL and import our movie data.mp4 48MB
  39. 06 - Install the MovieLens dataset into HDFS using the Ambari UI.mp4 48MB
  40. 09 - How MapReduce distributes processing.mp4 48MB
  41. 38 - Use Sqoop to export data from Hadoop to MySQL.mp4 47MB
  42. 73 - Kafka explained.mp4 46MB
  43. 55 - Install Phoenix and query HBase with it.mp4 46MB
  44. 60 - YARN explained.mp4 46MB
  45. 51 - Overview of Drill.mp4 45MB
  46. 74 - Setting up Kafka and publishing some data.mp4 43MB
  47. 37 - Use Sqoop to import data from MySQL to HDFS_Hive.mp4 43MB
  48. 66 - Oozie explained.mp4 42MB
  49. 63 - Mesos explained.mp4 41MB
  50. 11 - Installing Python, MRJob, and nano.mp4 41MB
  51. 77 - Set up Flume and publish logs with it.mp4 40MB
  52. 10 - MapReduce example - break down movie ratings by rating score.mp4 40MB
  53. 69 - Use Zeppelin to analyze movie ratings, part 1.mp4 40MB
  54. 68 - Zeppelin overview.mp4 40MB
  55. 15 - Introducing Ambari.mp4 39MB
  56. 21 - Compare your results to mine!.mp4 39MB
  57. 64 - ZooKeeper explained.mp4 38MB
  58. 28 - Exercise - Filter the lowest-rated movies by number of ratings.mp4 37MB
  59. 57 - Overview of Presto.mp4 37MB
  60. 90 - Sample application - consume webserver logs and keep track of top sellers.mp4 37MB
  61. 40 - What is HBase.mp4 36MB
  62. 91 - Sample application - serving movie recommendations to a website.mp4 36MB
  63. 70 - Use Zeppelin to analyze movie ratings, part 2.mp4 35MB
  64. 31 - Use Hive to find the most popular movie.mp4 35MB
  65. 16 - Introducing Pig.mp4 34MB
  66. 30 - What is Hive.mp4 34MB
  67. 54 - Overview of Phoenix.mp4 34MB
  68. 71 - Hue overview.mp4 32MB
  69. 22 - Why Spark.mp4 32MB
  70. 93 - Solution - Design a system to report daily sessions.mp4 31MB
  71. 08 - MapReduce - what it is and how it works.mp4 31MB
  72. 88 - Review - how the pieces fit together.mp4 31MB
  73. 82 - Solution - Aggregating HTTP access codes with Spark Streaming.mp4 31MB
  74. 83 - Apache Storm - Introduction.mp4 29MB
  75. 87 - The best of the rest.mp4 29MB
  76. 12 - Code up the ratings histogram MapReduce job and run it.mp4 28MB
  77. 89 - Understanding your requirements.mp4 28MB
  78. 04 - Tips for using this course.mp4 28MB
  79. 72 - Other technologies worth mentioning.mp4 28MB
  80. 50 - Choose a database for a given problem.mp4 28MB
  81. 32 - How Hive works.mp4 26MB
  82. 76 - Flume explained.mp4 26MB
  83. 33 - Exercise - Use Hive to find the movie with the highest average rating.mp4 24MB
  84. 35 - Integrating MySQL with Hadoop.mp4 23MB
  85. 85 - Flink - an overview.mp4 22MB
  86. 13 - Exercise - Rank movies by their popularity.mp4 21MB
  87. 23 - The Resilient Distributed Dataset (RDD).mp4 21MB
  88. 19 - More Pig Latin.mp4 20MB
  89. 20 - Exercise - Find the most-rated, one-star movie.mp4 19MB
  90. 53 - Querying across multiple databases.mp4 18MB
  91. 25 - Datasets and Spark 2.0.mp4 17MB
  92. 61 - Tez explained.mp4 14MB
  93. 34 - Compare your solution to mine.mp4 14MB
  94. 92 - Exercise - Design a system to report web sessions per day.mp4 7MB
  95. FreeCoursesOnline.Me.html 108KB
  96. FTUForum.com.html 100KB
  97. Discuss.FTUForum.com.html 32KB
  98. How you can help Team-FTU.txt 235B
  99. NulledPremium.com.url 163B
  100. Torrent Downloaded From GloDls.to.txt 84B