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Big Data (Hadoop) Developer Course outline
Introduction to Big data and Hadoop
o Understanding Big Data
o Challenges in processing Big Data
o 3V Characteristics (Volume, Variety and Velocity)
o Brief history of Hadoop
o How Hadoop addresses Big Data?
o HDFS and MR
o Hadoop echo system
HDFS (Hadoop Distributed File System)
o HDFS Overview and Architecture
o HDFS Keywords like Name Node, Data Node, Heart Beat etc
o Configuring HDFS
o Data Flows (Read and Write)
o HDFS Permissions and Security
o HDFS commands
o Rack Awareness
o 5 Daemons processes
Map Reduce
o Map Reduce Basics
o Map Reduce Data Flow
o Word count Example solving
o Algorithms for simple and complex problems
o Hadoop Streaming
Developing a Map Reduce Application
o Setting up working environment
o Custom Data types (Writable and Custom Key types)
o Input and Output file formats
o Driver, Mapper and Reducer Code Wal thru
o Configuring IDE Eclipse
o Writing Unit test and running locally
o Map Reduce Web UI
o Hands -on
How Map Reduce works?
o Classic Map Reduce (Map Reduce I)
o YARN (Map Reduce II)
o Job Scheduling

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o Shuffle and Sort
o Failures
o Oozie Workflows
o Hands-on Excercises
How Map Reduce works?
o Map Reduce Types
o Input formats – Input splits & records, text input, binary input, multiple inputs and
database input.
o Output formats - text output, binary output, multiple outputs, Lazy output and database
output.
o Hands-on
Hadoop Echo Systems
o PIG
 Overview of PIG
 Installation and running PIG
 PIG Latin
 Loading and storing data
 Hands-on
o HIVE
 Overview of HIVE
 Installation and running HIVE
 HiveQL
 Tables
 Hands-on
o HBASE
 Overview of HBASE
 Installation
 CLinets (avro, REST, Thrift)
 Hands-on
o SQOOP
 Overview of SQOOP
Solving Case studies
Courses Offerings
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