This document provides an overview of Hadoop, an open source framework for distributed storage and processing of large datasets. It discusses what Hadoop is, its applications and architecture, advantages like scalability and fault tolerance, and disadvantages such as security concerns. The document also outlines when Hadoop should be used, such as for large datasets that don't fit on a single machine or for extracting, transforming and loading large amounts of data. Key components of Hadoop include MapReduce, HDFS, YARN and its wider ecosystem of related projects.

3. CONTENT
 Introduction
 What is Hadoop?
 Hadoop Applications
 Hadoop Architecture
 Importance
 Advantages
 Disadvantages
 When to use Hadoop?
 Reference
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4.  Hadoop is an Apache open source
framework written in java that allows
distributed processing of large datasets
across clusters of computers using simple
programming models.
 A Hadoop frame-worked application works in
an environment that provides distributed
storage and computation across clusters of
computers.
INTRODUCTION
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5.  Hadoop is sub-project of Lucene (a
collection of industrial-strength search tools),
under the umbrella of the Apache Software
Foundation.
 Hadoop parallelizes data processing across
many nodes (computers) in a compute
cluster, speeding up large computations and
hiding I/O latency through increased
concurrency.
WHAT IS HADOOP?
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6.  Making Hadoop Applications More Widely
Accessible
 A Graphical Abstraction Layer on Top of
Hadoop Applications
HADOOP APPLICATIONS
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7. HADOOP ARCHITECTURE
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8.  Ability to store and process huge amounts of
any kind of data, quickly
 Computing power
 Fault tolerance
 Flexibility
 Low cost
 Scalability
WHY IS HADOOP IMPORTANT?
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9.  Scalable
 Cost effective
 Flexible
 Fast
 Resilient to failure
ADVANTAGES OF HADOOP
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10.  Security Concerns
 Not Fit for Small Data
 Potential Stability Issues
 General Limitations
DISADVANTAGES
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11. CONTRIBUTIONS 2006 - 2011
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12.  Hadoop Common (formerly Hadoop Core)
 Hadoop MapReduce
 Hadoop YARN (MapReduce 2.0)
 Hadoop Distributed File System (HDFS)
“CORE” HADOOP
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13.  Ambari, Zookeeper (managing & monitoring)
 HBase, Cassandra (database)
 Hive, Pig (data warehouse and query language)
 Mahout (machine learning)
 Chukwa, Avro, Oozie, Giraph, and many more
THE WIDER HADOOP ECOSYSTEM
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14.  Generally, always when “standard tools” don’t work
anymore because of sheer data size
(rule of thumb: if your data fits on a regular hard
drive, your better off sticking to
Python/SQL/Bash/etc.!)
 Aggregation across large data sets: use the power
of Reducers!
 Large-scale ETL operations (extract, transform,
load)
WHEN TO USE HADOOP?
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15. REFERENCE
 www.google.com
 www.wikipedia.com
 www.studymafia.org
 www.projectsreports.org

16. Thank You
ALL