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

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    IT COURSES

    Online live claseswww.imaginelife.in

    PH:8499068708 :8341832707EMAIL:[email protected]

    Hadoop

    http://www.imaginelife.in/http://www.imaginelife.in/http://www.imaginelife.in/
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    Topics To Be covered in Hadoop courseIntroduction to Big Data & Hadoop

    1.what is Big data?2.what are the challenges for processing bigdata?3.what is Hadoop?

    4.way Hadoop?5.History of Hadoop6.Use cases of Hadoop7.Hadoop eco System

    8.HDFS9.Mapreduce10.Statistics

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    Understanding the cluster 1.Typical workflow2.Writing files to HDPS3.Reading files from HDFS4.Rack Awareness5.5 Daemons

    6.HDFS commands HANDS-onInstalling the cluster 1.CDH4 Pseudo cluster2.CDH4 Multi node cluster

    3.configuration4.cluster on EC2 cloud5.How to use AWS EMR6.Hands on Exercises

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    Routine Admin and Monitoring Activities 1.Meta Data and Data Backups2.commissioning and Decommissioning nodes3.Recover from Namenode Failure4.Namenode High Availability5.Monitoring using ganglia and NagiosLets talk MapReduce 1.Before MapReduce

    2.MapReduce Overview3.word count problem4.word count flow and solution5.MapReduce flow

    6.Algorithms for simple problems7.Algorithms for complex problems

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    Developing the MapReduce Application 1.Data types2.File Formats3.Explain the Driver,Mapper and Reducer code4. configuring Development environment-Eclipse5.Writing Unit Test6.Running locally7.Hands on exercises

    How Map Reduce Works 1.Anatomy of map Reduce job run2.Job Submission3.Job initialization

    4.Task Assignment5.Job completion

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    6.Job Scheduling7.Job Failures8.shuffle and sort9.Oozie Workflows10.Hands on ExercisesMapReduce Types and Formats 1.MapReduce Types

    3.output Formats text Output, binary output,multiple outputs4.Lazy output and database output5.Hands-on Exercises

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    MapReduce Features 1.Counters2.Joins-map side and Reduce Side

    3.Sorting4.MapReduce combiner5.MapReduce partitioner6.MapReduce Distributed Cache7.Hands-on ExercisesHive 1. What is Hive?2.what Hive is not?3.Hive Architecture

    4.SQL vs Hive QL5.Data Types6.Managed Tables and External Tables7.partitions8.Buckets

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    9.Storage formats10.serDes11.importing Data12.Joins-map side and Reduce Side13. UDFs14.Hands-on ExercisesImapla 1.Need for RTQ

    2.impala Overview3.Impala Architecture4.Hands-on Exercises

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    Pig 1.What is Pig? Why Pig?2.Running pig3.Data4.pig Latin Statements5.Schemas6.Validations7.Functions and Macros

    8.UDFs9.When to Use pig and HIVE10.Hands-on Exercises

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    NoSQL and HBase 1.Why noSQL?2.Problems with RDBMS3.cap theorem4.HBase Concepts5.Use Cases for HBase6.HBase Data Model7.HBase Shell

    8.HBase Architecture9.Minor & major Compaction10.Bloom Filter&Block cache11.Schema Design12.Hands-0n Exercises

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    Sqoop 1.What is Sqoop?2.Motivation3.Sqoop Commands4.Importing Data toHDFSHIVEHBase5.Exposing Data6.Sqoop Connectors7.Hands on ExercisesFLUME

    1.What is Flume?2.Use Cases3.Flume Topology Source,Channel and Sink4.Hands-on Execises Ingest Data from twitter and Analyze WithHive

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    Machine Learning and Mahout 1.3Cs of Machine Learning2.Introduction to Mahout3.Hands-on Exercise:Build a Recommendation System usingMahoutPOCs 1.Banking Use case2.Telecom Use case