Cloudera Developer Training for Apache Hadoop - SF bay area - Feb 6-9

Hands-On Labs
Throughout the course, hands-on labs help students build their knowledge and apply the concepts being discussed. By the end of the course, participants will be able to import and analyze their own data in Apache Hadoop.
Labs include:
Importing flat-file data into HDFS
Running MapReduce jobs
Writing MapReduce code in Java, or using the Hadoop Streaming API
Importing data into HDFS from relational database management systems
Implementing an inverted index in Hadoop
Manipulating data with Hive and Pig
Creating pipelines of MapReduce jobs with Oozie
This four-day training course from Cloudera is for developers who want to learn to use Apache Hadoop to build powerful data processing applications.
You will learn:
How MapReduce and the Hadoop Distributed File System work
How to write MapReduce code in Java or other programming languages
What issues to consider when developing MapReduce jobs
How to implement common algorithms in Hadoop
Best practices for Hadoop development and debugging
How to leverage other project such as Apache Hive, Apache Pig, Sqoop and Oozie
Advanced Hadoop API topics required for real-world data analysis
Certification Exam
Following the training, attendees will have an opportunity to take become a Cloudera Certified Developer for Apache Hadoop (CCDH).
Course Pre-Requisites
This course is designed for developers with some programming experience (preferably Java). Existing knowledge of Hadoop is not required.
Course Contents
The course covers the following topics:
The Motivation For Hadoop
Problems with traditional large-scale systems
Requirements for a new approach
Hadoop: Basic Concepts
An Overview of Hadoop
The Hadoop Distributed File System
Hands-On Exercise
How MapReduce Works
Hands-On Exercise
Anatomy of a Hadoop Cluster
Other Hadoop Ecosystem Components
Writing a MapReduce Program
The MapReduce Flow
Examining a Sample MapReduce Program
Basic MapReduce API Concepts
The Driver Code
The Mapper
The Reducer
Hadoop's Streaming API
Using Eclipse for Rapid Development
Hands-On Exercise
Integrating Hadoop Into The Workflow
Relational Database Management Systems
Storage Systems
Importing Data from RDBMSs With Sqoop
Hands-On Exercise
Importing Real-Time Data with Flume
Accessing HDFS Using FuseDFS and Hoop
Delving Deeper Into The Hadoop API
Using Combiners
Using LocalJobRunner Mode for Faster Development
Reducing Intermediate Data with Combiners
The configure and close methods for MapReduce Setup and Teardown
Writing Partitioners for Better Load Balancing
Directly Accessing HDFS
Using The Distributed Cache
Hands-On Exercise
Common MapReduce Algorithms
Sorting and Searching
Indexing
Machine Learning with Mahout
Term Frequency - Inverse Document Frequency
Word Co-Occurrence
Hands-On Exercise
Using Hive and Pig
Hive Basics
Pig Basics
Hands-On Exercise
Practical Development Tips and Techniques
Testing with MRUnit
Debugging MapReduce Code
Using LocalJobRunner Mode for Easier Debugging
Retrieving Job Information with Counters
Logging
Splittable File Formats
Determining the Optimal Number of Reducers
Map-Only MapReduce Jobs
Implementing Multiple Mappers using ChainMapper
Hands-On Exercise
More Advanced MapReduce Programming
Custom Writables and WritableComparables
Saving Binary Data using SequenceFiles and Avro Files
Creating InputFormats and OutputFormats
Hands-On Exercise
Joining Data Sets in MapReduce Jobs
Map-Side Joins
The Secondary Sort
Reduce-Side Joins
Hands-On Exercise
Graph Manipulation in Hadoop
Introduction to graph techniques
Representing Graphs in Hadoop
Implementing a sample algorithm: Single Source Shortest Path
Creating Workflows with Oozie
The Motivation for Oozie
Oozie's Workflow Definition Format
Hands-On Exercise
Cloudera Certified Developer Exam