Course Content
01 – Learn all the buzzwords! And install the Hortonworks Data Platform Sandbox
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001 Udemy 101 Getting the Most From This Course.mp4
02:10
002 Tips for Using This Course.mp4
01:09
005 Installing Hadoop [Step by Step].mp4
17:44
006 The Hortonworks and Cloudera Merger, and how it affects this course.mp4
03:01
007 Hadoop Overview and History.mp4
07:44
008 Overview of the Hadoop Ecosystem.mp4
16:46
02 – Using Hadoop’s Core HDFS and MapReduce
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001 HDFS What it is, and how it works.mp4
13:53
003 Installing the MovieLens Dataset.mp4
06:20
004 [Activity] Install the MovieLens dataset into HDFS using the command line.mp4
07:50
005 MapReduce What it is, and how it works.mp4
10:40
006 How MapReduce distributes processing.mp4
12:57
007 MapReduce example Break down movie ratings by rating score.mp4
11:35
009 [Activity] Installing Python, MRJob, and nano.mp4
13:19
010 [Activity] Code up the ratings histogram MapReduce job and run it.mp4
07:36
011 [Exercise] Rank movies by their popularity.mp4
07:06
013 [Activity] Check your results against mine!.mp4
08:23
03 – Programming Hadoop with Pig
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001 Introducing Ambari.mp4
09:49
002 Introducing Pig.mp4
06:25
003 Example Find the oldest movie with a 5-star rating using Pig.mp4
15:07
004 [Activity] Find old 5-star movies with Pig.mp4
09:40
005 More Pig Latin.mp4
07:34
006 [Exercise] Find the most-rated one-star movie.mp4
01:55
007 Pig Challenge Compare Your Results to Mine!.mp4
05:37
04 – Programming Hadoop with Spark
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001 Why Spark.mp4
10:06
002 The Resilient Distributed Dataset (RDD).mp4
10:14
003 [Activity] Find the movie with the lowest average rating – with RDD’s.mp4
15:33
004 Datasets and Spark 2.0.mp4
06:28
005 [Activity] Find the movie with the lowest average rating – with DataFrames.mp4
10:00
006 [Activity] Movie recommendations with MLLib.mp4
12:43
007 [Exercise] Filter the lowest-rated movies by number of ratings.mp4
02:51
008 [Activity] Check your results against mine!.mp4
06:40
05 – Using relational data stores with Hadoop
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001 What is Hive.mp4
06:31
002 [Activity] Use Hive to find the most popular movie.mp4
10:45
003 How Hive works.mp4
09:10
004 [Exercise] Use Hive to find the movie with the highest average rating.mp4
01:55
005 Compare your solution to mine.mp4
04:10
006 Integrating MySQL with Hadoop.mp4
08:00
008 [Activity] Install MySQL and import our movie data.mp4
07:45
009 [Activity] Use Sqoop to import data from MySQL to HFDSHive.mp4
07:01
010 [Activity] Use Sqoop to export data from Hadoop to MySQL.mp4
07:16
06 – Using non-relational data stores with Hadoop
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001 Why NoSQL.mp4
13:54
002 What is HBase.mp4
12:55
003 [Activity] Import movie ratings into HBase.mp4
13:28
004 [Activity] Use HBase with Pig to import data at scale.mp4
11:19
005 Cassandra overview.mp4
14:50
007 [Activity] Installing Cassandra.mp4
10:53
008 [Activity] Write Spark output into Cassandra.mp4
11:00
009 MongoDB overview.mp4
17:19
010 [Activity] Install MongoDB, and integrate Spark with MongoDB.mp4
12:44
011 [Activity] Using the MongoDB shell.mp4
07:48
012 Choosing a database technology.mp4
15:59
013 [Exercise] Choose a database for a given problem.mp4
05:00
07 – Querying your Data Interactively
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001 Overview of Drill.mp4
07:55
002 [Activity] Setting up Drill.mp4
10:58
003 [Activity] Querying across multiple databases with Drill.mp4
07:07
004 Overview of Phoenix.mp4
08:55
005 [Activity] Install Phoenix and query HBase with it.mp4
07:02
006 [Activity] Integrate Phoenix with Pig.mp4
11:45
007 Overview of Presto.mp4
06:39
008 [Activity] Install Presto, and query Hive with it.mp4
12:26
009 [Activity] Query both Cassandra and Hive using Presto.mp4
09:01
08 – Managing your Cluster
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001 YARN explained.mp4
10:01
002 Tez explained.mp4
04:56
003 [Activity] Use Hive on Tez and measure the performance benefit.mp4
08:35
004 Mesos explained.mp4
07:13
005 ZooKeeper explained.mp4
13:10
006 [Activity] Simulating a failing master with ZooKeeper.mp4
06:47
007 Oozie explained.mp4
11:56
008 [Activity] Set up a simple Oozie workflow.mp4
16:54
009 Zeppelin overview.mp4
05:01
010 [Activity] Use Zeppelin to analyze movie ratings, part 1.mp4
12:27
011 [Activity] Use Zeppelin to analyze movie ratings, part 2.mp4
09:46
012 Hue overview.mp4
08:07
013 Other technologies worth mentioning.mp4
04:35
09 – Feeding Data to your Cluster
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001 Kafka explained.mp4
09:48
002 [Activity] Setting up Kafka, and publishing some data.mp4
07:24
003 [Activity] Publishing web logs with Kafka.mp4
10:21
004 Flume explained.mp4
10:16
005 [Activity] Set up Flume and publish logs with it.mp4
07:45
006 [Activity] Set up Flume to monitor a directory and store its data in HDFS.mp4
09:11
10 – Analyzing Streams of Data
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001 Spark Streaming Introduction.mp4
14:27
002 [Activity] Analyze web logs published with Flume using Spark Streaming.mp4
14:20
003 [Exercise] Monitor Flume-published logs for errors in real time.mp4
02:02
004 Exercise solution Aggregating HTTP access codes with Spark Streaming.mp4
04:24
005 Apache Storm Introduction.mp4
09:27
006 [Activity] Count words with Storm.mp4
15:49
007 Flink An Overview.mp4
06:53
008 [Activity] Counting words with Flink.mp4
10:20
11 – Designing Real-World Systems
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001 The Best of the Rest.mp4
09:24
002 Review How the pieces fit together.mp4
06:29
003 Understanding your requirements.mp4
08:02
004 Sample application consume webserver logs and keep track of top-sellers.mp4
10:06
005 Sample application serving movie recommendations to a website.mp4
11:18
006 [Exercise] Design a system to report web sessions per day.mp4
02:52
007 Exercise solution Design a system to count daily sessions.mp4
04:24
12 – Learning More
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001 Books and online resources.mp4
05:32
[Last updated 11/2023] The Ultimate Hands-On Hadoop: Tame your Big Data! (Udemy – Vietsub and Engsub)
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