hadoop training | Corporate| Online | Classroom | Mumbai

Hadoop Developer Training Outline

Introduction
Hadoop history and concepts
Ecosystem
Distributions
High level architecture
Hadoop myths
Hadoop challenges (hardware / software)
HDFS
Concepts (horizontal scaling, replication, data locality, rack awareness)
Architecture
Namenode (function, storage, file system meta-data, and block reports)
Secondary namenode
HA Standby namenode
Data node
Communications / heart-beats
Block manager / balancer
Health check / safemode
read / write path
Navigating HDFS UI
Command-line interaction with HDFS
File systems abstractions
WebHDFS
Reading / writing files using Java API
Getting HDFS stats
Data integrity
Compression
Benchmarking HDFS
Latest in HDFS
Namenode HA and Federation
HDFS roadmap
MapReduce
Parallel computing before MapReduce
MapReduce concepts
Daemons: jobtracker / tasktracker
Phases: driver, mapper, shuffle/sort, and reducer
First MapReduce job
MapReduce UI walk through
Counters
Distributed cache
Combiners
Partitioners
MapReduce configuration
Job config
MR types and formats
Sorting
Joins (map side & reduce side)
Job schedulers
MapReduce best practices
MRUnit
Optimizing MapReduce
Fool proofing MR
Thinking in MapReduce
YARN: architecture and use
Pig
Intro: principles and uses cases
Tools and environment
Example applications
Pig versus MapReduce
Hive
Intro: principles and uses cases
Environment and configuration
Example applications
HIve versus MapReduce
Hive versus Pig
HBase
History and concepts
Architecture
HBase versus RDBMS
HBase shell
HBase Java API
Splits and compaction
Read path / write path
Bloom filters and block indexes
Schema design
HBase MapReduce
Coprocessors.

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