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   Know more About "Hadoop" Course Content...

      

    1.     Introduction                                                                                      
      Hadoop is a biggest frame work to process petabytes of data in a faster and efficient manner. Hadoop
      supports both structured and unstructured data. Whereas Data Warehouse and currently popular
      BI Systems supports only structured data.That too digging data from huge quantity is really causes high
      latency in the traditional data warehouse.
      Topics Covered:

      • HDFS
      • HADOOP IO
      • MAP REDUCE
      • PIG
      • HIVE
      • HIVEQL
      • HBASE
      • SQOOP
      • FLUME
      • NOSQL
      • OOZIE
      • CASCADING

      HDFS:  It is a distributed file
      system in Hadoop
      Frame work.
      The HDFS architecture
       enables organizations
       to store bulk volumes
       of structured and
      unstructured data.
      Sub-Topics:
      ·               Hadoop Name Node
      ·               Hadoop Data Node
      ·               Hadoop high Availability
      ·               HDFS FEDERATION
      ·               READ ANATOMY
      ·               WRITE ANATOMY
      HADOOP IO
      Sub-Topics:
      ·               Compression
      ·               Serialization
      ·               APACHE AVRO

      Reading and writing sequence file
      Example: Unstructured data like Email messages, email server logs, Facebookmessages, and web log
       database log, images,videos, audios etc
      Map Reduce :
      Map Reduce is a framework to distribute the work in to tasks across multiple
      nodes and enables the system to process all tasks parallel and collect
      results in high speed.
      Sub-Topics:
      ·               How Map Reduce works
      ·               Map Reduce Unit Testing
      ·               Failures
      ·               Job scheduling
      ·               Map Reduce types
      ·               Input formats
      ·               Output formats
      ·               Counters
      ·               Map side joins
      ·               Reduce side joins
      PIG:
      Is a dataflow language in Hadoop
      Environment and it writes hidden
      Map Reduce code when the pig minimized code compiled.
      (Ex: instead of writing 100 lines of
      JAVA MapReduce Code, you can achieve it by simplified script of PIG in 10 Lines)
      Sub-Topics:
      ·               Installation
      ·               Running
      ·               Pig Latin
      ·               User defined functions
      ·               Operators
      HIVE:
      It is Data Warehouse in Hadoop frame work
      Sub-Topics:
      ·               Installation
      ·               Running
      HIVEQL (Hive Query Language)
      It is similar to Sql of RDBMS but slight differences are there.
      Sub-Topics:
      ·               Data types
      ·               DDL
      ·               DML
      ·               Functions
      ·               User defined functions
      ·               Operators

      HBASE:
      Is a columnar database in Hadoop Frame work.
      Sub-Topics:
      ·               Installation
      ·               Running
      ·               Hbasevs RDBMS

      SQOOP:
       Used for database connections,same style we export data from Hadoop to databases also.
      Sub-Topics:
      ·               Installation
      ·               Running
      ·               Importing
      FLUME:
      Is a powerful and friendly system to work with streaming data.
      NoSql:
      Is a beautiful concept,to work with bulk data aggregations. Since No Sql we store rows as columns.
      OOZIE:
      Oozie is workflow/coordination system to manage apache Hadoop jobs.
      Sub-Topics:
      ·               Defining oozie workflow
      ·               Packaging oozie job
      ·               Deploying oozie job
      ·               Running oozie job

       

      CASCADING:
      Sub-Topics:
      ·               Advanced JAVA API for MAPREDUCE.
      ·               Defining source
      ·               Defining sink
      ·               Defining trap
      ·               Functions
      ·               Filters
      ·               Aggregators
      ·               Buffers
      ·               Subassembly


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