Best Hadoop Training in Bangalore

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Introduction to Hadoop

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Hadoop Training

• Candidates, who are looking for the successful
  career path, must prefer Hadoop Training. Hadoop
  training helps you a lot in the career
  development.

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What is Hadoop?

• In the Information Technology, Hadoop is
  considered as the collection of large amounts of
  data and various tasks are performed. Hadoop is
  the open source software framework and it is the
  free Java based programming framework. It runs
  the applications on the cluster commodity
  hardware. Tasks are handled in the concurrent
  way. It allows distributed processing of huge
  amount of data by using simple programming
  models. Hadoop is the part of Apache Software
  Project, which was sponsored by Apache Software
  Foundation.

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What is the Need to prefer Hadoop?

• Hadoop has designed to scale up from the single
  servers to the thousands of machines. It offers
  storage and local computation.

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Challenges in Big Data Storage and Analysis

• It is slow to process and cant be scaled.
• Hard Drive Capacity to Process
• Unreliable Machines Risk
• Concurrent Execution of Tasks
• Reliability
• Secured and Easy to Use

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What is Hadoop Commons?

• With the help of this Hadoop commons, it provides
  access to the file system. The package of Hadoop
  commons contains the necessary JAR files and also
  the scripts that are used to start Hadoop. The
  package provides the documentation, source code
  and contribution section. It allows the project
  from the Hadoop Community.

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Architecture of Hadoop

• It was designed and built on two independent
  frameworks. They are HDFS and Map Reduce
• Hadoop Distributed File System (HDFS)
• Hadoop MapReduce

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What is Hadoop Distributed File System?

• A distributed file system is based on the GFS as
  its the shared file system. GFS is known as
  Google File System. The architecture of the HDFS
  divides the files into large portion that is up
  to 64 MB. It is distributed across the data
  servers. It has the datanode as well as
  namenodes. It helps in the storage and file
  system.

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What is Map Reduce?

• Map Reduce is the framework that is used for high
  performance distributed data processing. It uses
  the divide and aggregate programming paradigm.
• Hadoop is having the master slave architecture.
  This is for both storage and processing.

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Hadoop Master and Slave Architecture

• Components of HDFS
• In this architecture, it has three components
  there are NameNode, DataNodes and Secondary
  NameNodes.
• NameNode is considered as the master of the
  system. It completely maintains directory and
  files. It also manages the blocks that are
  present on the dataNodes.
• DataNodes are considered as Slave that is
  deployed on each of the machine. It is
  responsible for checking read and writes requests
  for the clients.
• Secondary NameNodes is for performing periodic
  check points. If there is the failure of the
  NameNode, then by using the check point you can
  restart the NameNode.

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• Components of Map Reduce
• They are two components in the Map Reduce. They
  are JobTracker and TaskTracker.
• JobTracker is the master of the system. It
  managers the resources in the most efficient way.
  It tries to schedule each map as very close to
  the actual data that is being processed.
• TaskTrackers is the slaves that are deployed on
  each machine. It is completely for running map
  and reduces the tasks. The instructor will be by
  the JobTracker.

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Environment of Hadoop

• The Hadoop environment will be in three modes.
• It is with the Standalone mode in which it helps
  in the debug of Hadoop applications
• Pseudo distributed It runs in different JVMs.
  It has the separate process, but all the process
  runs in the single machine.
• Fully Distributed It has parallel processing,
  workflow management, fault tolerance as well as
  data consistency.

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Features of Hadoop

• It is completely open source and written in Java
• Stores structured as well as Semistructured data
• Storage Capacity is high
• Detection of Failure and solves the problem
  itself only
• Cost Effective

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Uses of Hadoop

• Search Purpose
• Data Warehouse
• Video and Image Analysis

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Thank You
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