ArchiTech IT

Course information

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Hadoop Online Training – Course Content
Training Objectives of Hadoop:
Hadoop Course will provide the basic concepts of MapReduce applications developed using Hadoop, including a close look at framework components, use of Hadoop for a variety of data analysis tasks, and numerous examples of Hadoop in action. This course will further examine related technologies such as Hive, Pig, and Apache Accumulo.
Target Students / Prerequisites:
Students must be belonging to IT Background and familiar with Concepts in Java and Linux.
Introduction, 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:
Examining a Sample MapReduce Program
With several examples
Basic API Concepts
The Driver Code
The Mapper
The Reducer
Hadoop’s Streaming API
Delving Deeper Into The Hadoop API:
More About ToolRunner
Testing with MRUnit
Reducing Intermediate Data With Combiners
The configure and close methods for Map/Reduce Setup and Teardown
Writing Partitioners for Better Load Balancing
Hands-On Exercise
Directly Accessing HDFS
Using the Distributed Cache
Hands-On Exercise
Performing several Hadoop jobs:
The configure and close Methods
Sequence Files
Record Reader
Record Writer
Role of Reporter
Output Collector
Processing video files and audio files
Processing image files
Processing XML files
Counters
Directly Accessing HDFS
ToolRunner
Using The Distributed Cache
Common MapReduce Algorithms:
Sorting and Searching
Indexing
Classification/Machine Learning
Term Frequency-Inverse Document Frequency
Word Co-Occurrence
Hands-On Exercise: Creating an Inverted Index
Identity Mapper
Identity Reducer
Exploring well known problems using MapReduce applications
Using HBase:
What is HBase?
HBase API
Managing large data sets with HBase
Using HBase in Hadoop applications
Hands-on Exercise
Using Hive and Pig:
Hive Basics
Pig Basics
Hands-on Exercise
Practical Development Tips and Techniques
Debugging MapReduce Code
Using LocalJobRunner Mode for Easier Debugging
Retrieving Job Information with Countries
Logging
Splittable File Formats
Determining the Optimal Number of Reducers
Map-Only MapReduce Jobs
Hands-on Exercise
Debugging MapReduce Programs:
Testing with MRUnit
Logging
Classification/Machine Learning
Advanced MapReduce Programming
A Recap of the MapReduce Flow
The Secondary Sort
CustomizedInputFormats and OutputFormats
Pipelining Jobs With Oozie
Map-Side Joins
Reduce-Side Joins
Joining Data Sets in MapReduce:
Map-Side Joins
The Secondary Sort
Reduce-Side Joins
Monitoring and debugging on a Production Cluster:
Counters
Skipping Bad Records
Rerunning failed tasks with Isolation Runner
Tuning for Performance in MapReduce:
Reducing network traffic with combiner
Partitioners
Reducing the amount of input data
Using Compression
Reusing the JVM
Running with speculative execution
Refactoring code and rewriting algorithms Parameters affecting Performance
Other Performance Aspects