hadoop training san diego | Training and placement in usa

Apache Hadoop Training Objectives

All attendees will:

Understand the principles of parallel computing
Understand Hadoop architecture (HDFS and MapReduce)
Define Big Data project architecture
Use additional Big Data tools (Pig, Hive, Flume, etc.)
Use HBase in the project
Apply Big Data patterns and best practices
Plan, implement, and maintain clusters
Pinpoint performance bottlenecks and fix them

Apache Hadoop 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
Advanced topics
Real-world architectures
Ingress/egress
MR design patterns
R for statistics
Mahout - predictive analytics
Future of Hadoop.
or full course details please visit our website www.hadooponlinetraining.net

Duration for course is 30 days or 45 hours and special care will be taken. It is a one to one training with hands on experience.

* Resume preparation and Interview assistance will be provided.
For any further details please 

contact India +91-9052666559
         Usa : +1-678-693-3475.

visit www.hadooponlinetraining.net


please mail us all queries to info@magnifictraining.com