Big Data latest Interview Questions.

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BIG DATA/ Hadoop Interview Questions
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1.What is Big Data?
Big data is data that exceeds the processing
capacity of traditional database systems. The
data is too big, moves too fast, or doesnt fit
the strictures of your database architectures. To
gain value from this data, you must choose an
alternative way to process it.
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2.What is NoSQL?
NoSQL is a whole new way of thinking about a
database. NoSQL is not a relational database. The
reality is that a relational database model may
not be the best solution for all situations. The
easiest way to think of NoSQL, is that of a
database which does not adhering to the
traditional relational database management system
(RDMS) structure. Sometimes you will also see it
revered to as 'not only SQL'.
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3.We have already SQL then Why NoSQL? 
NoSQL is high performance with high availability,
and offers rich query language and easy
scalability.NoSQL is gaining momentum, and is
supported by Hadoop, MongoDB and others. The
NoSQL Database site is a good reference for
someone looking for more information.
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4.What is Hadoop and where did Hadoop come
from?
By Mike Olson The underlying technology was
invented by Google back in their earlier days so
they could usefully index all the rich textural
and structural information they were collecting,
and then present meaningful and actionable
results to users. There was nothing on the market
that would let them do that, so they built their
own platform. Googles innovations were
incorporated into Nutch, an open source project,
and Hadoop was later spun-off from that. Yahoo
has played a key role developing Hadoop for
enterprise applications.
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5.What problems can Hadoop solve?
By Mike Olson The Hadoop platform was designed
to solve problems where you have a lot of data
perhaps a mixture of complex and structured data
and it doesnt fit nicely into tables. Its for
situations where you want to run analytics that
are deep and computationally extensive, like
clustering and targeting. Thats exactly what
Google was doing when it was indexing the web and
examining user behavior to improve performance
algorithms.
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6.What is the Difference between Hadoop and
Apache Hadoop?
There is no diff, Hadoop, formally called
Apache Hadoop, is an Apache Software Foundation
project.
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7.Why would NoSQL be better than using a SQL
Database? And how much better is it?
It would be better when your site needs to scale
so massively that the best RDBMS running on the
best hardware you can afford and optimized as
much as possible simply can't keep up with the
load. How much better it is depends on the
specific use case (lots of update activity
combined with lots of joins is very hard on
"traditional" RDBMSs) - could well be a factor of
1000 in extreme cases.
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8.Name the modes in which Hadoop can run?
Hadoop can be run in one of three modesi.
Standalone (or local) modeii. Pseudo-distributed
modeiii. Fully distributed mode
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9.What do you understand by Standalone (or
local) mode?
There are no daemons running and everything runs
in a single JVM. Standalone mode is suitable for
running MapReduce programs during development,
since it is easy to test and debug them.
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10.What is the idea behind HDFS? Where does
HDFS fail?
HDFS is built around the idea that the most
efficient approach to storing data for processing
is to optimize it for write once, and read many
approach. Cannot support large number of small
files as the file system metadata increases with
every new file, and hence it is not able to scale
to billions of files. This file system metadata
is loaded into memory and since memory is
limited, so is the number of files supported.
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11.What are the ways of backing up the
filesystem metadata?
There are 2 ways of backing up the filesystem
metadata which maps different filenames with
their data stored as different blocks on various
data nodesWriting the filesystem metadata
persistently onto a local disk as well as on a
remote NFS mount.Running a secondary namenode.
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12.What are the functions of JobTracker in
Hadoop?
Once you submit your code to your cluster, the
JobTracker determines the execution plan by
determining which files to process, assigns nodes
to different tasks, and monitors all tasks as
they are running. If a task fail, the JobTracker
will automatically relaunch the task, possibly on
a different node, up to a predefined limit of
retries. There is only one JobTracker daemon per
Hadoop cluster. It is typically run on a server
as a master node of the cluster.
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13.What is MapReduce in Hadoop?
Hadoop MapReduce (Hadoop Map/Reduce) is a
software framework for distributed processing of
large data sets on compute clusters of commodity
hardware. It is a sub-project of the Apache
Hadoop project. The framework takes care of
scheduling tasks, monitoring them and
re-executing any failed tasks.
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14.What are the benefits of block transfer?
A file can be larger than any single disk in the
network. There is nothing that requires the
blocks from a file to be stored on the same disk,
so they can take advantage of any of the disks in
the cluster. Making the unit of abstraction a
block rather than a file simplifies the storage
subsystem. Blocks provide fault tolerance and
availability. To insure against corrupted blocks
and disk and machine failure, each block is
replicated to a small number of physically
separate machines (typically three). If a block
becomes unavailable, a copy can be read from
another location in a way that is transparent to
the client.
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15.What is the meaning of speculative
execution in Hadoop? Why is it important?
Speculative execution is a way of coping with
individual Machine performance. In large clusters
where hundreds or thousands of machines are
involved there may be machines which are not
performing as fast as others. This may result in
delays in a full job due to only one machine not
performaing well. To avoid this, speculative
execution in hadoop can run multiple copies of
same map or reduce task on different slave nodes.
The results from first node to finish are used.
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