Learn Big Data HADOOP Online Training in Hyderabad | Bangalore | India - Imaginelife

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Hadoop

• IT COURSES

• www.imaginelife.in
• PH8499068708 8341832707
• EMAILsuport_at_imaginelife.in

• Online live classes

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• Topics To Be covered in Hadoop course
• Introduction to Big Data Hadoop
• 1.what is Big data?
• 2.what are the challenges for processing big
  data?
• 3.what is Hadoop?
• 4.way Hadoop?
• 5.History of Hadoop
• 6.Use cases of Hadoop
• 7.Hadoop eco System
• 8.HDFS
• 9.Mapreduce
• 10.Statistics

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• Understanding the cluster
• 1.Typical workflow
• 2.Writing files to HDPS
• 3.Reading files from HDFS
• 4.Rack Awareness
• 5.5 Daemons
• 6.HDFS commands HANDS-on
• Installing the cluster
• 1.CDH4 Pseudo cluster
• 2.CDH4 Multi node cluster
• 3.configuration
• 4.cluster on EC2 cloud
• 5.How to use AWS EMR
• 6.Hands on Exercises

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• Routine Admin and Monitoring Activities
• 1.Meta Data and Data Backups
• 2.commissioning and Decommissioning nodes
• 3.Recover from Namenode Failure
• 4.Namenode High Availability
• 5.Monitoring using ganglia and Nagios
• Lets talk MapReduce
• 1.Before MapReduce
• 2.MapReduce Overview
• 3.word count problem
• 4.word count flow and solution
• 5.MapReduce flow
• 6.Algorithms for simple problems
• 7.Algorithms for complex problems

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• Developing the MapReduce Application
• 1.Data types
• 2.File Formats
• 3.Explain the Driver,Mapper and Reducer code
• 4. configuring Development environment-Eclipse
• 5.Writing Unit Test
• 6.Running locally
• 7.Hands on exercises
• How Map Reduce Works
• 1.Anatomy of map Reduce job run
• 2.Job Submission
• 3.Job initialization
• 4.Task Assignment
• 5.Job completion

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• 6.Job Scheduling
• 7.Job Failures
• 8.shuffle and sort
• 9.Oozie Workflows
• 10.Hands on Exercises
• MapReduce Types and Formats
• 1.MapReduce Types
• 3.output Formats text Output, binary
  output,multiple outputs
• 4.Lazy output and database output
• 5.Hands-on Exercises

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• MapReduce Features
• 1.Counters
• 2.Joins-map side and Reduce Side
• 3.Sorting
• 4.MapReduce combiner
• 5.MapReduce partitioner
• 6.MapReduce Distributed Cache
• 7.Hands-on Exercises
• Hive
• 1. What is Hive?
• 2.what Hive is not?
• 3.Hive Architecture
• 4.SQL vs Hive QL
• 5.Data Types
• 6.Managed Tables and External Tables
• 7.partitions
• 8.Buckets

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• 9.Storage formats
• 10.serDes
• 11.importing Data
• 12.Joins-map side and Reduce Side
• 13. UDFs
• 14.Hands-on Exercises
• Imapla
• 1.Need for RTQ
• 2.impala Overview
• 3.Impala Architecture
• 4.Hands-on Exercises

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• Pig
• 1.What is Pig? Why Pig?
• 2.Running pig
• 3.Data
• 4.pig Latin Statements
• 5.Schemas
• 6.Validations
• 7.Functions and Macros
• 8.UDFs
• 9.When to Use pig and HIVE
• 10.Hands-on Exercises

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• NoSQL and HBase
• 1.Why noSQL?
• 2.Problems with RDBMS
• 3.cap theorem
• 4.HBase Concepts
• 5.Use Cases for HBase
• 6.HBase Data Model
• 7.HBase Shell
• 8.HBase Architecture
• 9.Minor major Compaction
• 10.Bloom FilterBlock cache
• 11.Schema Design
• 12.Hands-0n Exercises

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• Sqoop
• 1.What is Sqoop?
• 2.Motivation
• 3.Sqoop Commands
• 4.Importing Data to
• HDFS
• HIVE
• HBase
• 5.Exposing Data
• 6.Sqoop Connectors
• 7.Hands on Exercises
• FLUME
• 1.What is Flume?
• 2.Use Cases
• 3.Flume Topology Source,Channel and Sink
• 4.Hands-on Execises Ingest Data from twitter and
  Analyze With Hive

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• Machine Learning and Mahout
• 1.3Cs of Machine Learning
• 2.Introduction to Mahout
• 3.Hands-on ExerciseBuild a Recommendation System
  using Mahout
• POCs
• 1.Banking Use case
• 2.Telecom Use case