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What is Hadoop?

Apache Hadoop is an open-source software framework that supports data-intensive distributed applications, licensed under the Apache v2 license. Apache Hadoop Online Training It supports the running of applications on large clusters of commodity hardware. Hadoop was derived from Google's MapReduce and Google File System (GFS) papers.

The Hadoop framework transparently provides both reliability and data motion to applications. Apache Hadoop Training Online  Hadoop implements a computational paradigm named MapReduce, where the application is divided into many small fragments of work, each of which may be executed or re-executed on any node in the cluster. Apache Hadoop Online Training In addition, it provides a distributed file system that stores data on the compute nodes, providing very high aggregate bandwidth across the cluster.

Hadoop was created by Doug Cutting and Mike Cafarella  in 2005. Hadoop Online Training  Doug, who was working at Yahoo at the time, named it after his son's toy elephant. It was originally developed to support distribution for the Nutch search engine project. Apache Hadoop Online Training Courses

Apache Hadoop Online Training:

Apache Hadoop Online Training Course Syllabus:

1.      Need for Hadoop

·         Introduction to Big Data

·         Problem with existing traditional system

·         Requirements for new approach

·         Comparing SQL databases and NOSQL(Hadoop)

2.      Hadoop Basic Concepts

·         An Overview of Hadoop

·         Configuring a Hadoop in Ubuntu OS

·         First example in Hadoop

3.      MapReduce

·         What is MapReduce?

·         Data flow in MapReduce

·         Map operation

·         Reduce operation

·         Real-world "MapReduce" problems

·         Execution strategies for MapReduce

4.      The Hadoop Distributed Filesystem

·         Namenodes

·         Datanodes

·         The Command-Line Interface

·         Reading and writing data using Java

·         Hadoop Archives

5.      Delving Deeper Into the Hadoop API

·         Using Combiners

·         Reducing Intermediate Data with Combiners

·         Writing Partitioners for Better Load Balancing

·         Directly Accessing HDFS

·         Hands-On Exercise

6.      Common MapReduce Algorithms

·         Sorting

·         Searching

·         Indexing

7.      Hadoop Optimizations

8.      Hadoop Best Practices

9.      Introduction to HBase

·         What is HBase?

·         HBase Architecture

·         HBase API

·         Managing large data sets with HBase

·         Using HBase in Hadoop applications

10.Introduction to Zookeeper

11.Summary

·         Sample Applications

·         References 

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