Video Analytics with Hadoop

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Insight on Performing the Video Analytics on
Hadoop! Data is widely available in two forms,
known as structured and unstructured. Considering
the present scenario where huge amount of data is
flooded every minute, everything about big data
video analytics needs to be understood. Let?s
learn more about the same. Big Data Video
Analytics If you have heard of the big data
courses in Delhi, you must have a little idea of
big data video analytics. There are various
analytics tools for use on the structured data
and analysis of unstructured data in the video
format is still an area needs to be discovered as
far as analysis is concerned. Use of video
recording gadgets has been rapidly growing which
as a result increasing the data and also the need
to analyze the same. A quick look at the data
gathered around the world shows that 80 of all
the data is available in unstructured format. The
challenge is that the presently available
analysis tools can only analyze the structured
data. Another data reveals that YouTube has been
getting uploads of a huge amount of video data
with each passing day. This huge number of data
needs another solid analytical tool for analysis.
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Significance of Hadoop Here Hadoop comes in
picture which plays an important role in solving
the issue of analysis of big video data. The
success of Hadoop in an analysis of structured
data naturally attracts the interest of various
stakeholders. They strongly believe the power of
Hadoop which can effectively analyze even the
unstructured video big data. Some of the
concepts known as Transcoding and MapReduce
Architecture are important and come handy to help
in the analysis of unstructured video big data.
However, using Hadoop comes with certain
limitations with regards to structured query
capabilities. Hadoop should also improve its
capabilities to be efficient to start the
analysis of the big data. Hadoop training in
Delhi can be really helpful in such
scenario. Digital devices which produce millions
of pixels in a flash are in the pockets of
billions of people around the world. If you look
around, there are other forms of video data other
than YouTube. These may include Surveillance
video recording etc. The video recording devices
further generate data which will need analysis.
There have been researchers working on to find
out how the analysis of the unstructured video
and image data will work. At most organizations,
the security devices operate 247 and archive the
recent ?hot data? for future investigation. An
ordinary enterprise will produce about a terabyte
of video with each passing day and that too, from
multiple sites around the office premises. Not
only this, there are companies that are getting
storage solutions to Fortune 500 clients. So we
can understand the amount of data being produced
every single minute. With such great amount of
data comes great responsibility of managing the
same and especially analysis. It has in turn,
given a rise to the need to manage huge amount of
video data. IT departments in large scale
enterprises are now uniting datasets which
currently store in silos. It is the high time we
dig into the datasets for the insight. Hadoop
institute in Delhi is helping people to deal with
the demand of the hour. They have various courses
that enable professionals learn how to analysis
the video data. Now software solutions more
concentrated on real time analytics including
motion detection and counting vehicles on
highways instead of the insight-specific
analytics or in-depth analytics. These solutions
are known for processing the video stream
efficiently and that too in the real time. It is
probably the only time when analytics algorithms
get in touch with these data. The metadata
generated will be related to triggering alarms
whereas the video data needs to be stored for a
short time in an archiving file system.
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Challenges Ahead! Now we have a fair idea about
the role of Hadoop in performing the video data
analysis. Even though, performing this with
Hadoop is not that easy. The challenges are ahead
which include Video Transcoder First and
foremost, the challenge which comes the way is to
decide on the way to deal with compressed video
data, suffering from various legacy limitations.
Long time back, the MPEG standard was recommended
for efficient encoding and decoding the sequence
of the image frames along with intra- frame
coding to provide high quality video streams
which is bounded by transmission bandwidth. The
main obstacle is that the MPEG could not predict
the Big Data revolution decades ago. Then, the
MPEG compressions appear unfriendly to mainstream
distributed systems like Hadoop or MPI. The
solution is the smart MapReduce jobs which can
seamlessly decode every video chunk on HDFS in a
distributed way. Video Analytics The video data
is required crunching into image frames and then
performing analytics on the data which is
Hadoop-friendly. No doubt, Hadoop MapReduce comes
as a strong scalable technology. It can be done
if it is carefully dissecting the typical video
analytics system. MapReduce enables to help in
providing linearly scalable performance that
needs little effort to craft parallelism. SQL
Analytics The most common investigation are done
post event that takes place by surveillance
video. The efforts are put in by security
officers who manually do this tiring task. Having
a strong video analytics platform can leverage
the structured insights Hadoop offers by using an
efficient query language like SQL. For more
details pls. visit https//www.madridsoftwaretrai
nings.com/hadoop.php