Introduction 1m 44s
Welcome 47s
What you should know before watching this course 39s
Using the exercise files 18s
1. Why Move Away from Relational Databases? 14m 19s
Understanding the limits of relational database management systems 5m 21s
Introducing CAP (consistency, availability, partitioning) 4m 6s
Understanding big data 4m 52s
2. What Is Hadoop? 13m 53s
Introducing Hadoop 9m 2s
Understanding the difference between HBase and Hadoop 3m 58s
Exploring the future of Hadoop 53s
3. Understanding Hadoop Core Components 39m 45s
Understanding Java virtual machines (JVMs) 1m 18s
Exploring Hadoop Distributed File System (HDFS) and other file systems 3m 6s
Introducing Hadoop cluster components 3m 49s
Exploring the Apache and Cloudera Hadoop distributions 2m 20s
Exploring the Hortonworks and MapR Hadoop distributions 2m 56s
Exploring Cloudera Hadoop via Cloudera Live 7m 15s
Understanding Hadoop versions 1m 58s
Using Amazon Web Services (AWS) and Microsoft cloud-hosted Hadoop 17m 3s
4. Setting Up the Hadoop Development Environment 17m 10s
Understanding the parts and pieces 4m 59s
Hosting Hadoop locally with the Cloudera developer distribution 2m 1s
Setting up the Cloudera Hadoop developer virtual machine (VM) 1m 3s
Adding Hadoop libraries to your test environment 1m 53s
Picking your programming language and IDE 1m 20s
Exploring the Cloudera VM IDE 5m 54s
5. Understanding MapReduce 1.0 48m 56s
Understanding MapReduce 1.0 11m 21s
Exploring the components of a MapReduce job 10m 2s
Working with the Hadoop file system 5m 37s
Using the console to run a MapReduce job 6m 23s
Reviewing the code for a MapReduce WordCount job 11m 9s
Running and tracking Hadoop jobs 4m 24s
6. Tuning MapReduce 20m 46s
Tuning by physical methods 6m 57s
Tuning a mapper 8m 2s
Tuning a reducer 2m 21s
Using a cache for lookups 3m 26s
7. Understanding MapReduce 2.0/YARN 7m 52s
Understanding MapReduce 2.0 4m 46s
Coding a basic WordCount in Java using MapReduce 2.0 8m 1s
Exploring advanced WordCount in Java using MapReduce 2.0 5m 5s
8. Understanding Hive 29m 9s
Introducing Hive and HBase 2m 55s
Understanding Hive 6m 37s
Revisiting WordCount using Hive 6m 31s
Understanding more about HQL query optimization 3m 9s
Exploring Hive tools in the Cloudera distribution and the Hue web console 9m 57s
9. Understanding Pig 19m 37s
Introducing Pig 5m 1s
Understanding Pig 4m 43s
Exploring use cases for Pig 3m 42s
Exploring Pig tools in the Cloudera distribution 6m 11s
10. Understanding Workflows and Connectors 23m 12s
Introducing Oozie 3m 7s
Building a workflow with Oozie 5m 24s
Introducing Sqoop 2m 36s
Importing data with Sqoop 3m 42s
Introducing Flume 3m 28s
Introducing ZooKeeper 3m 0s
Using ZooKeeper to coordinate workflows 1m 55s
11. Other Hadoop Libraries 17m 39s
Introducing Impala 4m 49s
Using Impala 3m 42s
Introducing Mahout 5m 52s
Introducing Storm 3m 16s
12. Understanding Spark 13m 42s
Introducing Apache Spark 4m 32s
Understanding the Hive landscape 9m 10s
13. Visualizing Hadoop Output with Tools 9m 54s
Using Tableau, Excel, and traditional tools 3m 45s
Using D3, BigML, and new tools 6m 9s
Conclusion 41s
Next steps 41s
Next steps 3m 50s

Hadoop is indispensible when it comes to processing big data—as necessary to understanding your information as servers are to storing it. This course is your introduction to Hadoop, its file system (HDFS), its processing engine (MapReduce), and its many libraries and programming tools. Developer and big-data consultant Lynn Langit shows how to set up a Hadoop development environment, run and optimize MapReduce jobs, code basic queries with Hive and Pig, and build workflows to schedule jobs. Plus, get a sneak peek at some up-and-coming libraries like Impala and the lightning-fast Spark.
Topics include:
Understanding Hadoop core components: HDFS and MapReduce
Setting up your Hadoop development environment
Working with the Hadoop file system
Running and tracking Hadoop jobs
Tuning MapReduce
Understanding Hive and HBase
Exploring Pig tools
Building workflows
Using other libraries, such as Impala, Mahout, and Storm
Understanding Spark
Visualizing Hadoop output