This presentation discusses the following features of Hadoop: Open source Fault Tolerance Distributed Processing Scalability Reliability   High Availability Economic Flexibility Easy to use Data locality Conclusion

1. Dr. C.V. Suresh Babu
(CentreforKnowledgeTransfer)
institute

2.  Open source
 Fault Tolerance
 Distributed Processing
 Scalability
 Reliability
 High Availability
 Economic
 Flexibility
 Easy to use
 Data locality
 Conclusion

3.  It is an open source Java-based
programming framework.
 Open source means it is freely available
and even we can change its source code
as per your requirements.
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4.  Hadoop control faults by the process of replica
creation.
 When client stores a file in HDFS, Hadoop framework
divide the file into blocks.
 Then client distributes data blocks across different
machines present in HDFS cluster.
 And, then create the replica of each block is on other
machines present in the cluster.
 HDFS, by default, creates 3 copies of a block on other
machines present in the cluster.
 If any machine in the cluster goes down or fails due to
unfavorable conditions.
 Then also, the user can easily access that data from
other machines.
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5.  Hadoop stores huge amount of data in a distributed manner in HDFS.
 Process the data in parallel on a cluster of nodes.
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6.  Hadoop is an open-source platform.
 This makes it extremely scalable platform.
 So, new nodes can be easily added without any downtime.
 Hadoop provides horizontal scalability so new node added on the
fly model to the system.
 In Apache hadoop, applications run on more than thousands of
node.
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7.  Data is reliably stored on the
cluster of machines despite
machine failure due to
replication of data.
 So, if any of the nodes fails,
then also we can store data
reliably.
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8.  Due to multiple copies of data,
data is highly available and
accessible despite hardware
failure.
 So, any machine goes down
data can be retrieved from the
other path.
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9.  Hadoop is not very expensive
as it runs on the cluster of
commodity hardware.
 As we are using low-cost
commodity hardware, we
don’t need to spend a huge
amount of money for scaling
out your Hadoop cluster
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10.  Hadoop is very flexible
in terms of ability to
deal with all kinds of
data.
 It deals with structured,
semi-structured or
unstructured.
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11.  No need of client to deal with
distributed computing, the
framework takes care of all the
things.
 So it is easy to use
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12.  It refers to the ability to move the
computation close to where actual
data resides on the node. Instead of
moving data to computation.
 This minimizes network congestion
and increases the over throughput of
the system.
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13.  Hadoop is highly fault-tolerant.
 It reliably stores huge amount of data
despite hardware failure.
 It provides High scalability and high
availability.
 Hadoop is cost efficient as it runs on a
cluster of commodity hardware.
 Hadoop work on Data locality as moving
computation is cheaper than moving data.
 All these features of Big data Hadoop
make it powerful for the Big data
processing.
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