Abstract time reductions, new product development and optimized


In this era,
the internet application and communication have seen a lot of expansion and
reputation in the field of Information Technology. These internet applications
and communication are continually generating the large size, different variety
and with some genuine difficult multifaceted structure data called big data. Currently,
new sources of data emerges that hold the potential to transform how
organisations drive, whether it be an advantage or a disadvantage to them. Today,
we are in the midst of yet another data-driven business revolution. New sources
of social media, mobile and sensor or machine-generated data hold the potential
to rewire an organizations value creation process.

We Will Write a Custom Essay Specifically
For You For Only $13.90/page!

order now


Big Data, Analytics, Cloud




Literal Definition

The term
Big Data is recognised for database systems. It is used for a number of technologies,
which help to organize data. It is mainly used in the field that studies
management and processing of any dataset too large for direct and individual
interpretation.  Oftentimes, storing
large quantities of data, introduces mechanical problems. The disk drives and
RAM cannot contain it all in one processer, so the databases have to be set in
to different parts and distributed across a number of systems. 

Why is Big Data Important?

importance of big data does not revolve around how much data you have, but what
you do with it. A person or organisation can take data from any source and
analyse it to find answers that enable cost reductions, time reductions, new
product development and optimized offerings, and smart decision-making. When
you combine big data with high-powered analytics, you can accomplish
business-related tasks such as:

root causes of failures, issues and defects in near-real time,

entire risk portfolios in minutes,

addition, detecting fraudulent behaviour before it affects a person or an


Big Data Analytics

Big Data Analytics
refers to the analysis through inspection, cleaning, transformation, models and
verification working towards the creation of conclusions and decision making on
the true meaning of the data. Data analytics examines large amount of data to
uncover hidden patterns, correlations and other insights. With today’s
advancement in technology, it is possible to analyse data and get answers from
it almost immediately – an effort that is slower and less efficient with more
traditional business intelligence solutions.


Storage System

The rapid
growth of data has restricted the capability of existing storage technologies
to store and manage data. Over the past few years, traditional


Data Management

One of the
most time-consuming and labour-intensive tasks of analytics is preparation of
data for analysis; a problem often exacerbated by Big Data as it stretches existing
infrastructure to its limits. Performing analytics on large volumes of data
requires efficient methods to store, filter, transform, and retrieve the data. Processing
big data involves the use of unconventional data management, data
representation and data compression methods for unstructured data. This in
turn, requires new database systems that are better suited for this kind of
data sets. Big data does not have well defined data models and schemas as in
relational database systems.

Cloud Computing

computing is a powerful technology to perform massive-scale and complex
computing. It eliminates the need to maintain expensive computing hardware,
dedicated space, and software. A fast-growing technology, which has established
itself in the next generation of the IT industry and business. Cloud services
have become a powerful architecture to perform complex large-scale computing
tasks and span of IT functions from storage and computation to database and
application services. The need to store, process and analyse large amount of
datasets has driven many organizations and individuals to adopt cloud

Mobile Cloud Computing

The increasing
popularity of wireless networks and mobile devices has taken cloud computing to
new heights because of the limited processing capability, storage capacity, and
battery lifetime of each device. This condition has led to the emergence of a
mobile cloud-computing paradigm. Mobile cloud facilities allow users to
outsource tasks to external service providers, for example, data can be
processed and stored out of a mobile device. Mobile cloud applications, such as
Gmail, iCloud, and Dropbox, have become prevalent recently.

Relationship between Cloud
Computing and Big Data

Cloud computing
and big data are conjoined. Big data provides users the ability to use
commodity computing to process distributed queries across multiple datasets and
return resultant sets in a timely manner. Cloud computing provides the
underlying engine through the use of Hadoop, a class of distributed
data-processing platforms.  

Big data
utilizes distributed storage technology based on cloud computing rather than
local storage attached to a computer or electronic device. Big data evaluation
is driven by fast-growing cloud-based applications developed using virtualized
technologies. Therefore, cloud computing not only provides facilities for the
computation and processing of big data but also serves as a service model.


Hadoop is
an open-source software framework for storing data and running applications on
clusters of commodity hardware. It provides massive storage for any kind of
data, enormous processing power and the ability to handle virtually limitless
concurrent tasks or jobs. Hadoop makes it possible to run applications on
systems with thousands of commodity hardware nodes, and to handle thousands of
terabytes of data. It’s distributed file system facilitates rapid data transfer
rates among nodes and allows the system to continue operating in case of a node

Case Studies

The relationship
between Big data and Cloud computing is contemplated by reported case studies
on big data using cloud computing technology.




Nokia is a
mobile communications company whose products comes to be an integral part of
people’s lives. Many people around the world use Nokia mobile phones to
communicate, capture photos and share experiences. Thus, Nokia gathers and analyses
large amounts of data from mobile phones. However, in order to support its
extensive use of big data. Nokia relies on a technology ecosystem that includes
a Teradata Enterprise Data Warehouse, numerous Oracle and MySQL data marts, visualization
technologies, and Hadoop. Nokia has over 100 Terabytes of structured data on
Teradata and petabytes of multistructured data on the Hadoop Distributed File System
(HDFS). The HDFS data warehouse allows the storage of all data and offers data
processing at the petabyte scale.


Google constantly
develops new products and services that have data algorithms. Google uses big
data to refine its core search and ad serving algorithms. Although these days
Google’s big data innovation goes well beyond basic search, it’s still their
core business. They process 3.5 billion requests per day and each request a
database of 20 billion web pages. This is refreshed daily, as Google’s bots
crawl the web, copying down what they see and taking it back to be stored in
Google’s index database. Google describes that the self-driving car is a big
data application. Using and generating massive amounts of data from sensors,
cameras, tracking devices and coupling this with on-board and real time data
analysis from Google Maps, Street-view and other sources allows the Google car
to safely drive on the roads without any input from a human driver.  


Big data
is a broad, rapidly evolving topic. While it is not well-suited for all types
of computing, many organizations are turning to big data for certain types of
work loads and using it to supplement their existing analysis and business
tools. Big data systems are uniquely suited for surfacing difficult-to-detect
patterns and providing insight into behaviours that are impossible to find
through conventional means. By correctly implement systems that deal with big
data, organizations can gain incredible value from data that is already


I'm Gerard!

Would you like to get a custom essay? How about receiving a customized one?

Check it out