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

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


1

Hadoop
  • IT COURSES
  • www.imaginelife.in
  • PH8499068708 8341832707
  • EMAILsuport_at_imaginelife.in
  • Online live classes

2
  • 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

3
  • 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

4
  • 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

5
  • 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

6
  • 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

7
  • 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

8
  • 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

9
  • 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

10
  • 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

11
  • 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

12
  • 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
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