Hadoop Online Training Institute in India,USA,UK,Canada.

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HADOOP ONLINE TUTORIAL

• By
• HYDERABADSYS ONLINE TRAINING

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Hadoop OnlineTraining Course Content

• Basics of Hadoop
• Motivation for Hadoop
• Large scale system training
• Survey of data storage literature
• Literature survey of data processing
• Networking constraints
• New approach requirements

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Basic concepts of HadoopWhat is Hadoop?

• What is Hadoop?
• Distributed file system of Hadoop
• Map reduction of Hadoop works
• Hadoop cluster and its anatomy
• Hadoop demons

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• Master demons
• Name node
• Tracking of job
• Secondary node detection
• Slave daemons

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Hadoop OnlineTraining Course Content

• Tracking of task
• HDFS(Hadoop Distributed File System)
• Spilts and blocks
• Input Spilts
• HDFS spilts
• Replication of data
• Awareness of Hadoop racking
• High availably of data
• Block placement and cluster architecture
• CASE STUDIES
• Practices Tuning of performances
• Development of mass reduce programs
• Local mode
• Running without HDFS

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• High availably of data
• Block placement and cluster architecture
• CASE STUDIES
• Practices Tuning of performances
• Development of mass reduce programs
• Local mode
• Running without HDFS

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Hadoop OnlineTraining Course Content

• Hadoop administration
• Setup of Hadoop cluster of Cloud era, Apache,
  Green plum, Horton works
• On a single desktop, make a full cluster of a
  Hadoop setup.
• Configure and Install Apache Hadoop on a multi
  node cluster.
• In a distributed mode, configure and install
  Cloud era distribution.
• In a fully distributed mode, configure and
  install Hortom works distribution

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• In a fully distributed mode, configure the Green
  Plum distribution.
• Monitor the cluster
• Get used to the management console of Horton
  works and Cloud era.
• Name the node in a safe mode
• Data backup.
• Case studies
• Monitoring of clusters

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Hadoop OnlineTraining Course Content

• Hadoop Development
• Writing a MapReduce Program
• Sample the mapreduce program.
• API concepts and their basics
• Driver code
• Mapper
• Reducer
• Hadoop AVI streaming

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• Performing several Hadoop jobs
• Configuring close methods
• Sequencing of files
• Record reading
• Record writer
• Reporter and its role
• Counters
• Output collection

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Hadoop OnlineTraining Course Content

• Assessing HDFS
• Tool runner
• Use of distributed CACHE
• Several MapReduce jobs (In Detailed)
• 1.MOST EFFECTIVE SEARCH USING MAPREDUCE
• 2.GENERATING THE RECOMMENDATIONS USING MAPREDUCE
• 3.PROCESSING THE LOG FILES USING MAPREDUCE
• Identification of mapper
• Identification of reducer
• Exploring the problems using this application
• Debugging the MapReduce Programs
• MR unit testing
• Logging

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• 3.PROCESSING THE LOG FILES USING MAPREDUCE
• Identification of mapper
• Identification of reducer
• Exploring the problems using this application
• Debugging the MapReduce Programs
• MR unit testing
• Logging

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• Hadoop OnlineTraining Course Content
• Debugging strategies
• Advanced MapReduce Programming
• Secondary sort
• Output and input format customization
• Mapreduce joins
• Monitoring debugging on a Production Cluster
• Counters

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• Skipping Bad Records
• Running the local mode
• MapReduce performance tuning
• Reduction network traffic by combiner
• Partitioners
• Reducing of input data
• Using Compression
• Reusing the JVM

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