The introduction of web, newer technologies and mobile
devices has caused a fundamental change to the nature of the data. Big data has
its own distinctive and unique qualities that has differentiated and
distinguished it from conventional corporate data. It is the information that
is no longer centralized, highly structured and well manageable now as compared
to the traditional form of loosely structured and distributed data. In general,
it contains the following mentioned attributes.
·
Volume – The volume of data developed both inside
and outside the firewall and corporations through web, infrastructure, mobile
devices and other sources is rising exponentially every year.
·
Speed – The speed at which this new data is
developed and the requirement for real time analytics to fetch business from it
is rising to the digitization of mobile computing, transactions and the number
of mobile device users and internet.
·
Type – The types of data are increasing at a
speedy pace mainly in form of semi structured and unstructured text based form including
log file data, location based data and social media data.
On a wider note, big data could be generated from a large
number of resources like mentioned in brief in below.
·
Social Media – At present, there are about 150
million public blogs, 260 million twitter users and 700 million Facebook
customers. Every tweet, blog post, comment and Facebook update creates multiple
new data points available in the form of structured, semi structured and
unstructured also known widely as data exhaust.
·
Mobile Devices – There are approximate 5 billion mobile
phones that are in use across the world. Each text, call and an instant message
is treated as data. Mobile devices especially tablets and smart phones make it
simpler to use data generating and other social media applications. Mobile
devices also gather and transmit location data.
·
Internet Transactions – Billions of stock trades,
transactions and online purchases used to happen on daily intervals comprising innumerous
automated transactions. Each develops a wide number of data points collected by
banks, retailers, credit agencies and credit card service providers.
·
Networked Devices and Sensors – Electronic devices
of all kinds comprising IT hardware, servers, smart energy meters and
temperature sensors develop semi structured updated log data that records every
action.
Traditional data management and other data
warehousing tools are not up to the mark of analyzing and processing big data in
a cost effective and timely manner. To be precise, data must be handled and
managed effectively into relational tables having neat rows and columns prior to
conventional enterprise data warehouse can absorb it. Due to the requirement of
manpower and time, incorporating such structure to bigger chunks of
unstructured data is quite illogical. In addition, scaling up a traditional enterprise
data warehouse to accommodate potentially Megabytes of data would need unrealistic
and authoritative financial investments in new as well as oftentimes proprietary
hardware requirements too. There could be many different ways used and applied to
analyze and process big data. They take benefit in general of commodity hardware
to enable scale out parallel processing strategies, bestow non relational data
storage capabilities in order to process semi structured and unstructured data
and apply modern data analytics and visualization technology to convey deep insights
to end users worldwide. Last but not least, IT firms and organizations like IBTechnology assist others in determining the most reliable and practical big
data use cases and create products and services by making use of latest
technologies and big data recommendation engine easier to implement, use and
manage along with helping out the customers in giving the flexibility required to
experiment with new big data tools and technologies.
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