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hadoop developer training
Contact :
Magnific training
+91-9052666559
info@magnifictraining.com
• Hadoop Developer Training: Introduction to
Apache Hadoop Development:
• Introduction to Apache Hadoop Development
training class teaches attendees how to build
distributed, data-intensive applications using
the Hadoop framework. Students learn the
principles of parallel programming and how
use Big Data tools such as Pig, Hive, and
HBase.
• 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.
hadoop developer training
• Apache Hadoop Training Outline
• Introduction
– Hadoop history and concepts
– Ecosystem
– Distributions
– High level architecture
– Hadoop myths.
hadoop developer training
• 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.
hadoop developer training
• Communications / heart-beats
• Block manager / balancer
• Health check / safemode
• read / write path
• Navigating HDFS UI
• Command-line interaction with HDFS
• File systems abstractions
• WebHDFS.
hadoop developer training
• 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
hadoop developer training
• Pig
– Intro: principles and uses cases
– Tools and environment
– Example applications
– Pig versus MapReduce
hadoop developer training
• Hive
– Intro: principles and uses cases
– Environment and configuration
– Example applications
– HIve versus MapReduce
– Hive versus Pig
hadoop developer training
• HBase
– History and concepts
– Architecture
– HBase versus RDBMS
– HBase shell
– HBase Java API
– Splits and compaction
– Read path / write path.

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Hadoop developer training

  • 1. hadoop developer training Contact : Magnific training +91-9052666559 info@magnifictraining.com
  • 2. • Hadoop Developer Training: Introduction to Apache Hadoop Development: • Introduction to Apache Hadoop Development training class teaches attendees how to build distributed, data-intensive applications using the Hadoop framework. Students learn the principles of parallel programming and how use Big Data tools such as Pig, Hive, and HBase.
  • 3. • 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.
  • 4. hadoop developer training • Apache Hadoop Training Outline • Introduction – Hadoop history and concepts – Ecosystem – Distributions – High level architecture – Hadoop myths.
  • 5. hadoop developer training • 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.
  • 6. hadoop developer training • Communications / heart-beats • Block manager / balancer • Health check / safemode • read / write path • Navigating HDFS UI • Command-line interaction with HDFS • File systems abstractions • WebHDFS.
  • 7. hadoop developer training • 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
  • 8. hadoop developer training • Pig – Intro: principles and uses cases – Tools and environment – Example applications – Pig versus MapReduce
  • 9. hadoop developer training • Hive – Intro: principles and uses cases – Environment and configuration – Example applications – HIve versus MapReduce – Hive versus Pig
  • 10. hadoop developer training • HBase – History and concepts – Architecture – HBase versus RDBMS – HBase shell – HBase Java API – Splits and compaction – Read path / write path.