Big Data Analytics in the Cloud for Business Intelligence.docx

Big Data Analytics in the Cloud for Business
Intelligence
K.Venkata Avinash
School of Computer Science And Engineering
The Research And Development Cell
Phagwara,India.
Abstract:
Cloud computing and big data analytics are,
without a doubt, two of the most important
technologies to enter the mainstream IT
industry in recent years. Surprisingly, the
two technologies are coming together to
deliver powerful results and benefits for
businesses. Cloud computing is already
changing the way IT services are provided
by so called cloud companies and how
businesses and users interact with IT
resources. Big Data is a data analysis
methodology enables by recent advances
in information and communications
technology. However, big data analysis
requires a huge amount of computing
resources making adoption costs of big
data technology is not affordable for many
small to medium enterprises. In this paper,
we outline the the benefits and challenges
involved in deploying big data analytics
through cloud computing. We argue that
cloud computing can support the storage
and computing requirements of big data
analytics. We discuss how the consolidation
of these two dominant technologies can
enhance the process of big data mining
enabling businesses to improve decision-
making processes. We also highlight the
issues and risks that should be addressed
when using a so called CLaaS, cloud-based
service model.
Keywords: Cloud Computing, Big
Data Analytics, Cloud Analytics, Security,
Privacy, Business Intelligence, MapReduce,
AaaS, CLaaS
o 1.Introduction :
The term Business Intelligence (BI)
refers to technologies, applications
and practices for the collection,
integration, analysis, and
presentation of business
information. The main purpose of
Business Intelligence is to support
better and faster business decision
making. Organizations are being
compelled to capture, understand
and harness their data to support
decision making in order to improve
business operationswas the first
company to properly use the
characteristics of cloud computing
and to provide their physical
resources as virtual resources to the
customers [73] followed by others.
Now different cloud platforms such
as Google App Engine, Windows

Azure, and Salesforce.com etc. are
available in the market, providing
cloud services, which can be utilized
by enterprise developers to develop
and migrate their application and
data, and benefit from cloud
computing.
In an ever-changing business world,
many companies now face growing
pressure to develop and ramp up
their business intelligence efforts
quickly and at a low cost in order to
remain competitive. Recently
emerged cloud computing is
changing the way IT services are
provided by companies and how
businesses and users interact with
IT resources. It represents a
paradigm shift that introduces
flexible service models that
companies can subscribe on a pay-
as-you-use model. The data in the
world is growing exponentially. Big
data is an evolving term that
describes any huge amount of
structured, semi-structured and
unstructured data that has the
potential to be mined for useful
information. Big data is data that
exceeds the processing capacity of
traditional databases. The data is
too big to be processed by a single
machine. The evolving field of big
data analytics examines large
amounts of data to uncover hidden
patterns, correlations and other
insights. Big data technology has
become possible with the latest
developments in computer
technology as well as algorithms
and approaches developed to
handle big data. In this paper, our
aim is to investigate the impacts of
cloud computing and big data on
businesses and analyse the benefits
and challenges it brings to
enterprises. First, we overview the
concepts, issues and technology of
cloud computing and big data
separately. We then present a
framework that combines these two
technologies to form an ideal
platform for e-commerce. We
discuss the role of big data in
enhancing the main functional areas
of e-commerce such as customer
management, marketing, payments,
supply chain and management.
2.RELATED WORK:
Cloud computing popularity has prompted
several academic and industry initiatives to
explore the capabilities and enhancements
in cloud computing. The value proposition
of cloud computing in comparison with on
premise investments is one of the key
research areas. There are several initiatives
to specifically address the security issues
and challenges in cloud computing. There
have been several academic initiatives
investigating e-business model aspects of
cloud computing. Aydin discusses research
of E-Commerce Based on Cloud
Computing. Dan and Roger compared
various cloud offerings such as Google App
Engine, Amazon EC2, and Microsoft Azure
to provide guidance on cost, application
performance (and limitations) for different
deployment scenarios. Agarwal et al
present various methods for handling the

problems of big data analysis through Map
Reduce framework over Hadoop
Distributed File System (HDFS). In this
paper, Map Reduce techniques have been
implemented for Big Data analysis using
HDFS. Yadav et al present an overview of
architecture and algorithms used in large
data sets. These algorithms define various
structures and methods implemented to
handle Big Data and this paper lists various
tools that were developed for analysing
them. It also describes about the various
security issues, application and trends
followed by a large data set. Fan and Bifet
present an overview of big data mining
outlining its current status, controversy,
and forecast to the future. This paper also
covers various interesting and state-of-the-
art topics on Big Data mining. Sharma and
Navdeti discuss about the big data security
at the environment level along with the
probing of built in protections. It also
presents some security issues that we are
dealing with today and proposes security
solutions and commercially accessible
techniques to address the same. The paper
also covers all the security solutions to
secure the Hadoop ecosystem. They also
provide an overview on big data, its
importance in our live and some
technologies to handle big data. Jassena
and David discuss issues, challenges and
solutions of big data mining. Padgavankar
and Gupta provide detail analysis of the
challenges involved in big data storage and
propose some solutions to handle them.
Jayasree provides an overview of big data
technologies such as MapReduce and
Hadoop and compares with traditional
data mining techniques. Zulkernine et al
presents a conceptual architecture for a
cloud based analytics as a service (CLaaS).
3.Cloud Computing:
3.1 Cloud Computing
Many researchers have defined
cloud computing differently. One
mostly accepted definition is given
by the United States Institute of
Standards (NIST). Per the NIST
definition , “Cloud computing is a
model for enabling ubiquitous,
convenient, on-demand network
access to a shared pool of
configurable computing resources
(e.g., networks, servers, storage,
applications, and services) that can
be rapidly provisioned and released
with minimal management effort or
service provider interaction. This
cloud model is composed of five
essential characteristics, five service
models, and four deployment
models”
3.2 Cloud Computing
Characteristics
Cloud computing has five essential
characteristics. They are on-demand
capabilities, broad network access,
resource pooling, rapid elasticity
and measured service. These are
the characteristics that distinguish it
from other computing paradigms.
On-demand Capabilities: A
consumer can unilaterally provision
computing capabilities, such as
server time and network storage, as
needed automatically without
requiring human interaction with
each service provider.

Broad network access: Capabilities
are available over the network and
accessed through standard
mechanisms that promote use by
heterogeneous thin or thick client
platforms (e.g., mobile phones,
tablets, laptops and workstations).
Resource Pooling: The provider's
computing resources are pooled to
serve multiple consumers using a
multi-tenant model, with different
physical and virtual resources
dynamically assigned and
reassigned per consumer demand.
Rapid elasticity: Capabilities can be
elastically provisioned and released,
in some cases automatically, to
scale rapidly outward and inward
commensurate with demand.
Measured service: Cloud systems
automatically control and optimize
resource use by leveraging a
metering capability at some level of
abstraction appropriate to the type
of service (e.g., storage, processing,
bandwidth and active user
accounts).
3.3Cloud Deployment
Models Cloud
deployment models are grouped
broadly into four models: private
cloud, public cloud, community
cloud and hybrid cloud. Private cloud
is the most secure way to utilize
cloud computing. The cloud
infrastructure is provisioned for
exclusive use by a single
organization comprising multiple
consumers (e.g., business units). It
may be owned, managed, and
operated by the organization, a
third party, or some combination of
them, and it may exist on or off
premises. Community cloud is
provisioned for exclusive use by a
specific community of consumers
from organizations that have shared
concerns. It may be owned,
managed, and operated by one or
more of the organizations in the
community, a third party, or some
combination of them, and it may
exist on or off premises. Public cloud
is provisioned for open use by the
public. It may be owned, managed,
and operated by a business,
academic, or government
organization, or some combination
of them. It exists on the premises of
the cloud provider. Hybrid cloud is a
composition of two or more distinct
cloud infrastructures (private,
community, or public) that remain
unique entities, but are bound
together by standardized or
proprietary technology that enables
data and application portability .
3.4 Cloud Service
Delivery Models
Cloud-based services are grouped
broadly into four models: Data as a
Service (DaaS), Software as a
Service (SaaS), Platform as a Service
(PaaS), and Infrastructure as a
Service (IaaS). Software as a Service
(SaaS) is a model that provides the
user with access to already
developer applications that are

running in the cloud. The access is
achieved by cloud clients and the
cloud users do not manage the
infrastructure where the application
resides, eliminating with this the
way the need to install and run the
application on the cloud user’s own
computers. Platform as a Service
(PaaS): is a model that delivers to
the user development environment
services where the user can develop
and run in-house built applications.
The services might include an
operating system, a programming
language execution environment,
databases and web servers.
Infrastructure as a Service (IaaS) is a
model that provides the user with
virtual infrastructure, for example
servers and data storage space.
Virtualization plays a major role in
this mode, by allowing IaaS-cloud
providers to supply resources on-
demand extracting them from their
large pools installed in data centres.
Data as a Service (DaaS) is a model
in which, data is readily accessible
through a Cloud-based platform.
Simply put, DaaS is a new way of
accessing business-critical data
within an existing data centre.
Figure 1 illustrates the general cloud
computing architecture.
3.5 Cloud Computing
Benefits
Cost Efficiency - This is the biggest
advantage of cloud computing, achieved
by the elimination of the investment in
stand-alone software or servers. By
leveraging cloud’s capabilities, companies
can save on licensing fees and at the same
time eliminate overhead charges such as
the cost of data storage, software updates,
management etc. Renting your
infrastructure can make good financial
sense. The pay as you go (PAYG) model is
especially Continuous availability - Public
clouds offer services that are available
wherever the end user might be located.
This approach enables easy access to
information and accommodates the needs
of users in different time zones and
geographic locations. As a side benefit,
collaboration booms since it is now easier
than ever to access, view and modify
shared documents and files. Moreover,
service uptime is in most cases
guaranteed, providing in that way
continuous availability of resources. The
various cloud vendors typically use
multiple servers for maximum redundancy.
In case of system failure, alternative
instances are automatically spawned on
other machines. Scalability and Elasticity -
Scalability is a built-in feature for cloud

deployments. Cloud instances are
deployed automatically only when needed
and thus, you pay only for the applications
and data storage you need. Hand in hand,
also comes elasticity, since clouds can be
scaled to meet your changing IT system
demands. Fast deployment and ease of
integration - A cloud-based application can
be up and running with just a few hours
rather than weeks or months and without
spending a large sum of money in advance.
This is one of the key benefits of cloud. On
the same aspect, the introduction of a new
user in the system happens
instantaneously, eliminating waiting
periods.
4. BIG DATA ANALYTICS
4.1 What is “Big Data
Big Data is the term for a collection of
data sets so large and complex that it
becomes difficult to process using
conventional data mining techniques and
tools. The overall goal of the big data
analytics is to extract useful information
from a huge data set and transform it into
an understandable structure for further
use. The major processes of big data
include capture, curation, storage, search,
sharing, transfer, analysis, and
visualisation. Recently the importance of
this field has attracted enormous attention
because it gives businesses useful
information and better insight of both
structured and unstructured data, which
may lead to betterinformed decision-
making . In a business context, big data
analytics is the process of examining “big
data” sets to uncover hidden patterns,
unknown correlations, market trends,
customer preferences and other useful
business information . Today’s advances in
technology combined with the recent
developments in data analytics algorithms
and approaches have made it possible for
organisations to take advantage big data
analytics. Some of the major issues in
applying big data analytics successfully
include data quality, storage, visualization
and processing . Some business examples
of big data are social media content,
mobile phone details, transactional data,
health records, financial documents,
Internet of things and weather
information.
4.2 Big Data Technologies
In order to support big data analytics, a
computing platform should meet the
following 3 criteria, so called 3 Vs as
illustrated in Figure 2. Variety: The
platform supports wide variety of data and
enables enterprises to manage this data as
is in its original format, and with extensive
transformation tools to convert it to other
desired formats. Velocity: The platform
can handle data at any velocity, either low-
latency streams, such as sensor or stock
data, or large volumes of batch data.
Volume: The platform can handle huge
volumes of at-rest or streaming data.

Traditional data mining involves finding
interesting patterns from datasets
whereas big data analytics involves large
scale storage and processing of huge data
sets. Traditionally Hadoop and MapReduce
are two of the popular technologies for big
data analytics . More tools and
technologies are becoming available for
big data processing. Examples include
Amazon’s Redshift hosted BI data
warehouse, Google’s BigQuery data
analytics service, IBM’s Bluemix cloud
platform and Amazon’s Kinesis data
processing service. The future state of big
data will be a hybrid of on-premises and
cloud, Alternatives to traditional SQL-
based relational databases, called NoSQL
(Not Only SQL) databases, are rapidly
gaining popularity as tools for use in
specific kinds of big data analytic
applications.
4.3 Big Data Benefits
The fact that the valuable enterprise data
will reside outside the corporate firewall
raises serious concerns. Some of the most
common challenges are discussed below
Cost reduction - Big data technologies like
Hadoop and cloud-based analytics can
provide substantial cost advantages. While
comparisons between big data technology
and traditional architectures (data
warehouses and marts) are difficult
because of differences in functionality, a
price comparison alone can suggest order-
of-magnitude improvements. Rather than
processing and storing vast quantities of
new data in a data warehouse, for
example, companies are using Hadoop
clusters for that purpose, and moving data
to enterprise warehouses as needed for
production analytical applications. Faster,
better decision making - Analytics has
always involved attempts to improve
decision making, and big data doesn’t
change that. Following the Big data
analytics really makes the business
managers good decision makers. Large
organizations are seeking both faster and
better decisions with big data, and they’re
finding them. Driven by the speed of
Hadoop and in-memory analytics, several
companies are focused on speeding up
existing decisions. New products and
services - Perhaps the most interesting use
of big data analytics is to create new
products and services for customers.
Online companies have done this for a
decade or so, but now predominantly
offline firms are doing it too. Product
recommendation - It is obviously very clear
that the adoption of big data and analytics
have proved to be a very powerful strategy
for online businesses. The influence of the
huge data of the customers on the
business is turning to be very significant
and economic tool for strengthening a
business. Storing and working on huge
data has been always a challenge for any
trade. Big data has constructed the road
for managing such huge data making
business much simpler and profitable.
Fraud Detection - High-performance
analytics is not just another technology
fad. It represents a revolutionary change in
the way organizations harness data. With
new distributed computing options like in-
memory processing on commodity
hardware, businesses can have access to a
flexible and scalable real-time big data
analytics solution at a reasonable cost. This
is sure to change the way insurance
companies manage big data across their
business – especially in detecting fraud

5. DEPLOYING BIG DATA
ANALYTICS IN THE CLOUD
Cloud-based big data analytics is a service
model in which elements of the big data
analytics process are provided through a
public or private cloud . It uses a range of
analytical tools and techniques to help
businesses extract information from
massive data and present it in a way that is
easily categorised and readily available via
a web browser. Such cloud-based data
analytics applications and services are
typically offered under a subscription-
based or utility (pay-per-use) pricing
model. This service model is called Cloud
Analytics as a Service (CLAaaS). In this
model, analytics is readily accessible
through a cloud computing platform. Such
cloud-based data analytics service will
enable businesses to automate processes
on an anytime, anywhere basis. Examples
of such cloud-based analytics products and
services include hosted data warehouses,
software-as-a-service business intelligence
(SaaS BI) and cloud-based social media
analytics. Data stored in a cloud-based
database can help businesses with their
decision making processes. With cloud-
based big data, analysts have not only
more data to work with, but also the
processing power to handle large numbers
of records with many attributes. This has
the ability to increase predictability. The
combination of big data and cloud
computing also lets analysts explore new
behavioural data such as websites visited
or location on a daily basis.
5.1 Major Benefits for
Business Organisations
On-demand self-service: As the name
describes, organisations can expand the
storage or service at a click of the button
without any human help. Organisations
will can establish big data infrastructure as
quickly as possible.
Data and Information over the net:
Information is available over the network
and can be accessed anytime through the
net by different devices such as laptop,
mobile, ipads etc.
Resource pooling: Provider resources are
grouped and used efficiently by multi-
tenant model. Resources include storage,
memory, VMs etc.
Rapid elasticity: Resources (both hardware
& software) can be increased or decreased
efficiently and effectively in quick span of
time. Customers can purchase the
resources for any quantity and at any time.
Cost effective: Resource usage can be
monitored and would be charged on the
basis of usage. This system is very
transparent which makes the provider and
the user more comfortable to adopt it. Big
data technologies such as Hadoop and

cloud-based analytics bring significant cost
advantages when it comes to storing large
amounts of data – plus they can identify
more efficient ways of doing business.
5.2 Big Data and Cloud
Computing Challenges
The fact that the valuable enterprise data
will reside outside the corporate firewall
raises serious concerns. Some of the most
common challenges are discussed below.
Data Storage - Storing and analysing large
volumes of data that is crucial for a
company to work requires a vast and
complex hardware infrastructure. With the
continuous growth of data, data storage
device is becoming increasingly more
important, and many cloud companies
pursue big capacity of storage to be
competitive.
Data Quality - Accuracy and timely
availability of data is crucial for decision-
making. Big data is only helpful when an
information management process is
implemented to guarantee data quality.
Security and Privacy - Security is one of the
major concerns with big data. To make
more sense from the big data,
organizations would need to start
integrating parts of their sensitive data
into the bigger data. To do this, companies
would need to start establishing security
policies which are self-configurable: these
policies must leverage existing trust
relationships, and promote data and
resource sharing within the organizations,
while ensuring that data analytics are
optimized and not limited because of such
policies.
Hacking and various attacks to cloud
infrastructure would affect multiple clients
even if only one site is attacked. These
risks can be mitigated by using security
applications, encrypted file systems, data
loss software, and buying security
hardware to track unusual behaviour
across servers.
Service Delivery and Billing It is difficult to
assess the costs involved due to the on-
demand nature of the services. Budgeting
and assessment of the cost will be very
difficult unless the provider has some good
and comparable benchmarks to offer. The
service-level agreements (SLAs) of the
provider are not adequate to guarantee
the availability and scalability. Businesses

will be reluctant to switch to cloud without
a strong service quality guarantee.
Interoperability and Portability Businesses
should have the leverage of migrating in
and out of the cloud and switching
providers whenever they want, and there
should be no lock-in period. Cloud
computing services should have the
capability to integrate smoothly with the
on premise IT.
Reliability and Availability Cloud providers
still lack round-the-clock service; this
results in frequent outages. It is important
to monitor the service being provided
using internal or third-party tools. It is vital
to have plans to supervise usage, SLAs,
performance, robustness, and business
dependency of these services.
Performance and Bandwidth Cost
Businesses can save money on hardware
but they must spend more for the
bandwidth. This can be a low cost for
smaller applications but can be
significantly high for the data-intensive
applications. Delivering intensive and
complex data over the network requires
sufficient bandwidth All these challenges
should not be considered as road blocks in
the pursuit of cloud computing. It is rather
important to consider these issues and the
possible ways out before adopting the
technologyGoogle.
6 CONCLUSIONS
Businesses have long used data analytics to
help direct their strategy to maximise
profits and support their decision-making
processes. Today it is widely accepted that
cloud computing and big data technologies
are two dominant technologies that will
shape up the business world. Cloud is no
longer just a buzzword – it’s a fact-of-life
affecting every facet of the technology
industry. Big data technologies provided
through cloud computing will allow
businesses to make proactive, knowledge-
driven decisions as it allows them to have
future trends and behaviours predicted.
Businesses will be able to store their data
remotely and access data and services
from anywhere and anytime. Further,
cloud-based data analytics provides the
infrastructure that companies would
otherwise have to build up themselves
from scratch. Alongside data analytics,
cloud computing is also capable of keeping
businesses stay competitive by providing
many benefits such as cost effectiveness,

resource pooling, on-demand service,
rapid elasticity, and ease of management.
Despite these benefits, there are some
challenges and drawbacks, particularly in
relation to privacy and security. Before
investing in cloud-based big data analytics,
an organisation needs to fully grasp the
extent of what’s involved. Investing in
cloud analytics can be profitable for an
organization but proper planning is
essential to ensure that all phases of
analytics elements are covered.
References :
Cloud Computing for E-Commerce, Journal of Mobile Computing and Application.[ Aydin, N. (2015)].
Talia, D. (2013). Clouds for Scalable Big Data Analytics. Published by IEEE Computer Society.
Fan, J., Han, F. & Liu, H., 2013. Challenges of Big Data Analysis. ResearchGate
Yadav, C. Wang, S. and Kumar M. (2013). “Algorithm and Approaches to handle large Data- A Survey”,
IJCSN
https://nessi.eu/Files/Private/NESSI_WhitePaper_BigData.pdf
http://www.edbt.org/Proceedings/2011-Uppsala/papers/edbt/a50-agrawal.pd
https://ijkie.org/IJKIE_December2014_IRINA%26HAO.pdf

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