Smac technical report

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A TECHNICAL REPORT WRITING ON
SMAC TECHNOLOGY (STACK)
BY
U.NAGA PRADEEP
For any assistancecanemail to :pne818@gmail.com

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Chapter -1
Introduction
Social,Mobility,AnalyticsandCloud(SMAC) are individual technologies and platforms which have risen during the past
few years and haves how nimmense growth While each of these four components have been evolving
individually,companies are beginning to treat them as an integrated whole The convergence on these technologies means
dismantling the traditiona lbusiness design: No longer is It required to keep people and information in the same location or
to spend big money to support information sharing,communication and collaboration.
SMAC based solutions,when offered and deployed as a SaaS based model,have given businesses a real
opportunity to develop innovative solutions that ultimately lead to leve raging public IT infrastructure,lowering
cost of ownership and deployment of innovative applications that not only improve enterprise decision making
capabilities but also allows them to roll out new unprecedented business models and increase their reach to
customers.
Social,mobility,analytics and cloud are reshaping businesses,consumers and all traditional approaches the
Indian IT-BPM industry has seen till now. Capitalizing on it sal ready well established IT/BPO and knowledge
service out sourcing industry,India is rising to play an import an trole a sake you sourcing destination for MNC
slooking to leverage the technologies and transform their business models.
Indian players there fore need to act quickly in the near future to develop relevant IP and build significant scale
to capture market share.Our view is that over the next 3 years technology M&A deals will tend to focus on
SMAC technologies.
As highlighted by NASSCOM,IDC estimates that Indian IT vendors could generate $225 billion in SMAC
related revenue by 2020 India already has many small but innovative players who are making break through in
these fields by creating products and solutions by leveraging the SMAC technologies.

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As the market matures these small players are going to look at being acquired or forming alliances with larger
players which would provide them the systems and processes enabling them to scale upto the levels required by
global enterprise customers Big ITBP Mplayers would need to acquire smaller players in niche segments in
order to develop domain expertise and also develop geographical presence.
Social links people to their friends, work and each other in new and unexpected ways. Mobile devices are a
platform for effective social networking and new ways to work. Analytics (Big Data) helps gain meaningful
insights from the information, facilitating informed decision making, Cloud enables delivery of information and
functionality to users and systems.
Fig 1:Images of Social, Mobile, Analytics and Cloud

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Chapter-2
Social Networking
Definition:
We define social network sites as web-based services that allow individuals to (1) construct a public or semi-
public profile within a bounded system, (2) articulate a list of other users with whom they share a connection,
and (3) view and traverse their list of connections and those made by others within the system. The nature and
nomenclature of these connections may vary from site to site.
Since their introduction, social network sites (SNSs) such as MySpace, Facebook, Cyworld, and Bebo have
attracted millions of users, many of whom have integrated these sites into their daily practices. As of this
writing, there are hundreds of SNSs, with various technological affordances, supporting a wide range of
interests and practices. While their key technological features are fairly consistent, the cultures that emerge
around SNSs are varied. Most sites support the maintenance of pre-existing social networks, but others help
strangers connect based on shared interests, political views, or activities. Some sites cater to diverse audiences,
while others attract people based on common language or shared racial, sexual, religious, or nationality-based
identities. Sites also vary in the extent to which they incorporate new information and communication tools,
such as mobile connectivity, blogging, and photo/video-sharing.
Scholars from disparate fields have examined SNSs in order to understand the practices, implications, culture,
and meaning of the sites, as well as users' engagement with them.
SNSs Hit the Mainstream:
From 2003 onward, many new SNSs were launched, prompting social software analyst Clay Shirky (2003) to
coin the term YASNS: "Yet Another Social Networking Service."Most took the form of profile-centric sites,
trying to replicate the early success of Friend ster or target specific demographics. While socially-organized
SNSs solicit broad audiences, professional sites such as LinkedIn, Visible Path, and Xing (formerly open BC)
focus on business people. "Passion-centric" SNSs like Dogster (T. Rheingold, personal communication, August
2, 2007) help strangers connect based on shared interests. Care2 helps activists meet, Couch surfing connects
travelers to people with couches, and My Church joins Christian churches and their members.
Furthermore, as the social media and user-generated content phenomena grew, websites focused on media
sharing began implementing SNS features and becoming SNSs themselves. Examples include Flickr (photo
sharing), Last.FM (music listening habits), and YouTube (video sharing).

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A Global Phenomenon:
While MySpace attracted the majority of media attention in the U.S. and abroad, SNSs were proliferating and
growing in popularity worldwide. Friendster gained traction in the Pacific Islands, Orkut became the premier
SNS in Brazil before growing rapidly in India (Madhavan, 2007), Mixi attained widespread adoption in Japan,
LunarStorm took off in Sweden, Dutch users embraced Hyves, Grono captured Poland, Hi5 was adopted in
smallercountries in Latin America, South America, and Europe, and Bebo became very popular in the United
Kingdom, New Zealand, and Australia. Additionally, previously popular communication and community
services began implementing SNS features.
The Chinese QQ instant messaging service instantly became the largest SNS worldwide when it added profiles
and made friends visible (McLeod, 2006), while the forum tool Cyworld cornered the Korean market by
introducing homepages and buddies (Ewers, 2006).
Blogging services with complete SNS features also became popular. In the U.S., blogging tools with SNS
features, such as Xanga, LiveJournal, and Vox, attracted broad audiences. Skyrock reigns in France, and
Windows Live Spaces dominates numerous markets worldwide, including in Mexico, Italy, and Spain.
Although SNSs like QQ, Orkut, and Live Spaces are just as large as, if not larger than, MySpace, they receive
little coverage in U.S. and English-speaking media, making it difficult to track their trajectories.
Bridging Online and Offline Social Networks:
Although exceptions exist, the available research suggests that most SNSs primarily support pre-existing social
relations. Ellison, Stein field, and Lampe (2007) suggest that Facebook is used to maintain existing offline
relationships or solidify offline connections, as opposed to meeting new people. These relationships may be
weak ties, but typically there is some common offline element among individuals who friend one another, such
as a shared class at school. This is one of the chief dimensions that differentiate SNSs from earlier forms of
public CMC such as newsgroups (Ellison et al., 2007).
Research in this vein has investigated how online interactions interface with offline ones. For instance, Lampe,
Ellison, and Steinfield (2006) found that Facebook users engage in "searching" for people with whom they have
an offline connection more than they "browse" for complete strangers to meet. Likewise, Pew research found
that 91% of U.S. teens who use SNSs do so to connect with friends (Lenhart & Madden, 2007).

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Chapter-3
Mobile Technologies
To helpful to begin to familiarize yourself with common mobile technology terms and concepts.
Airtime: The time spent talking or otherwise using the voice function of a mobile device; mobile carriers
determine billing charges based on the airtime used.
Android: Popular operating system developed by Google that is used on many smartphones
and tablets. Android’s main competition in the smartphone market is Apple’s iOS used on iPhones and iPads.
App: A small, specialized piece of software that can be downloaded onto a mobile device.“App” is short for
“application.”
Basic phone: Device with basic phone functionality (e.g., SMS and voice), very limited computing power, few
connectivity options, and a basic user interface and numeric keypad.
Crowd sourcing: Obtaining information or input into a particular task or project by enlisting
the services of a number of people, either paid or unpaid, typically via the Internet.
Graphical user interface (GUI): The screen that a user interacts with to operate a mobile
device. Basic phones and some feature phones are display only. Other feature phones, smartphones, and tablets
use larger touch screens for richer interaction and usability.
Feature phone: Midrange mobile device with a graphical user interface, basic apps, and
more numerous connectivity options than a basic phone, but without a smartphone’s computing power and
QWERTY keyboard.
GIS: Geographic information system. A platform designed to capture, edit, analyze,and visualize geographic or
spatial data, usually via a map.
GPRS: General packet radio service. A data transmission system similar to SMS but without limits on the
number of characters or transmission size.
GPS: Global positioning system. A network of satellites that broadcast signals read by Hand held GPS units or
other GPS-enabled mobile devices to calculate a precise location using
latitude and longitude coordinates.
GSM: Global system for mobiles. The most widely used cell phone technology globally. GSM compliant
phones have removable SIM cards, which enable you to transfer your mobile subscription account, contacts,
and other data from one GSM phone to another.

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ICT4D: Information communication and technology for development. The use of mobile and
computing devices to improve development outcomes.
IMEI: International mobile equipment identity. The unique identification number found printed inside the
battery compartment or listed under a device’s settings menu.
Java: Popular operating system for a feature phone and some smart phones.
Mobile platform: The system that receives the data sent from mobile devices. The various mobile platforms
have varied features and functions, including data storage, data verification, data analysis, and data reporting.
QWERTY: The standard layout of an English keyboard, with the letters q, w, e, r, t, and y positioned in that
order, reading from left to right, on the top row of alphabetic characters.
SIM card: Subscriber identity module. A small card inserted into a mobile device on which phone numbers,
contact information, and other data are stored.
Skip logic: Instructions programmed into a mobile-based questionnaire that will present a different series of
questions to the user based on a previous response. Also known as conditional branching.
Smartphone: High-end, full-featured mobile device with touch screen graphical user interface, on-screen or
hard-button QWERTY keypad, advanced computing power, download able apps, GPS receiver, and multiple
connectivity options.
SMS: Short message service. System for sending short messages of a fixed length traditionally a maximum of
160 characters in English, with other lengths in other languages.
Tablet: Full-featured mobile device with large touchscreen graphical user interface, on-screen
keyboard, advanced computing power, downloadable apps, GPS receiver, and multipleconnectivity options.
Tablets typically lack SMS and voice communication options.

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Mobile technology has already proven itself a powerful and efficient tool that accelerates achievement of
project objectives and ultimately of development goals.
Efficiency and data quality gains have been accepted as the norm for many applications, and the frontier of
possibilities expands daily.
Given the dynamic and evolving nature of the mobile technology field, the following chapters are not
exhaustive and do not aspire to be. But whether you are a practitioner in the field or a project manager at
headquarters, you will learn in these pages how to begin developing and putting in place a mobile technology
strategy for development programming and how to select mobile platforms; case studies will show you how
mobile technology has been used for development.
What Is Information Communication Technology for Development?
ICT4D is the application of common, everyday technologies to development. This report focuses on the mobile
technology (specifically, on phones and tablets) and how they are being used around the world to accelerate
development goals. Projects from diverse technical areas have adopted mobile technology to perform:
 Survey data collection
 Routine M&E
 Dissemination of general or targeted information
 Awareness and sensitization campaigns
 Patient tracking
 Surveillance and reporting
 Crowd sourcing
 Logistics management information systems
 Mobile banking and cash transfers
 Coordinating service delivery or referral services
 Conflict early warning systems
 Public dialogue
What Are the Advantages of Mobile Data Technology?
The advantages of using mobile data technology over traditional, paper-based methods
include:
 Faster, more accessible data—Data collected in the field with mobile devices can be submitted and
stored online or on home office servers, allowing for real-time access to data for analysis and decision
making.
 More reliable data—Collecting data in the field with mobile devices eliminates the need
to enter data from paper into a database, therefore reducing the opportunity for error or loss.
 Cleaner data—Mobile applications embed skip logic that directs respondents to specific
sections of the questionnaire based on previous responses (e.g., different questions for males and
females). Thus, data is never entered where it doesn’t belong.

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 Ability to monitor data collection—By monitoring GPS locations and time stamps of
submitted surveys, you can track field worker performance and the quality of incoming data.
 Richer data—Integrated GPS coordinate collection, camera capabilities, and audio capture broaden the
scope and type of information that may be collected.
 Built-in data visualization—In some systems, online interfaces permit data visualization
or have dashboards that allow data to be manipulated, customized, and downloaded.
 More than just data—Mobile devices support a range of capabilities beyond data collection
(e.g., mobile banking, patient tracking, and crowd sourcing, as well as communication.
Data Collection: Mobile Features and Functions:
Your data requirements will influence your choice of mobile device. Many basic phones on the market today
come with features previously considered advanced, such as the capacity to take photos and videos. However,
the quality of the images you collect may not be comparable in quality to those of smartphones and other
mobile devices with more advanced technology. Also, basic phones may not be as reliable or as durable as more
advanced devices.
Some features, such as Internet access and the ability to collect GPS data, are available only on
more technologically advanced mobile devices. Screen size may also be a factor.
In addition, keep in mind that although Android devices are compatible with most mobile platforms, certain
mobile devices are incompatible with certain mobile platforms; consult your platform requirements before
settling on a specific device.
FEATURES AND FUNCTIONS TO CONSIDER
 GPS to collect location data.
 A camera to collect photos. Consider number of pixels you require for adequate image
quality.
 Ability to take and store videos on the device.
 Data storage capacity. In areas with poor network connections, mobile users may need to
 initially store collected data on the phone until they can transmit the data.
 Ability to block certain uses of the phones and data. This may involve setting a password to prevent
unauthorized app downloads or an app-blocker that restricts access to in appropriate
 apps, games, etc.

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 Internet capacity.
 A touch screen.
Mobile Technology Platforms:
A number of mobile platforms offer a broad range of features and different pricing plans several open-source
options come at no cost.To determine which platform is right for your mobile initiative, consider your needs
and overall project budget. To a certain extent, your project’s data needs and the frequency and type of data
collection outlined in your mobile strategy will help you narrow your options. Please keep in mind that
platforms are changing rapidly and that features and price structures evolve over time; the latest information is
always available on the platform’s Web site.When deciding among mobile platforms, consider: phone
requirements; the data entry interface and transmission methods permitted; data storage, analysis, and reporting
features; miscellaneous features; and pricing structure.

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Chapter-4
Analytics (Big Data)
Introducing Big Data:
Big Data has to deal with large and complex datasets that can be structured, semi-structured, or unstructured
and will typically not fit into memory to be processed. They have to be processed in place, which means that
computation has to be done where the data resides for processing. When we talk to developers, the people
actually building Big Data systems and applications, we get a better idea of what they mean about 3Vs. They
typically would mention the 3Vs model of Big Data, which are velocity, volume, and variety.
Velocity refers to the low latency, real-time speed at which the analytics need to be applied. A typical
example of this would be to perform analytics on a continuous stream of data originating from a social
networking site or aggregation of disparate sources of data.
Volume refers to the size of the dataset. It may be in KB, MB, GB, TB, or PB based on the type of the
application that generates or receives the data.
Variety refers to the various types of the data that can exist, for example, text, audio, video, and photos.
Big Data usually includes datasets with sizes. It is not possible for such systems to process this amount of data
within the time frame mandated by the business. Big Data volumes are a constantly moving target, as of 2012
ranging from a few dozen terabytes to many petabytes of data in a single dataset. Faced with this seemingly
insurmountable challenge, entirely new platforms are called Big Data platforms.
Fig2:BigData
Getting information about popular organizations that hold Big Data:
Some of the popular organizations that hold Big Data are as follows:
 Facebook: It has 40 PB of data and captures 100 TB/day.

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 Yahoo!: It has 60 PB of data.
 Twitter: It captures 8 TB/day.
 EBay: It has 40 PB of data and captures 50 TB/day.
How much data is considered as Big Data differs from company to company. Though true that one company's
Big Data is another's small, there is something common: doesn't fit in memory, nor disk, has rapid influx of data
that needs to be processed and would benefit from distributed software stacks. For some companies, 10 TB of
data would be considered Big Data and for others 1 PB would be Big Data. So only you can determine whether
the data is really Big Data. It is sufficient to say that it would start in the low terabyte range. Also, a question
well worth asking is, as you are not capturing and retaining enough of your data do you think you do not have a
Big Data problem now? In some scenarios, companies literally discard data, because there wasn't a cost
effective way to store and process it. With platforms as Hadoop, it is possible to start capturing and storing all
that data.
Introducing Hadoop:
Apache Hadoop is an open source Java framework for processing and querying vast amounts of data on large
clusters of commodity hardware. Hadoop is a top level Apache project, initiated and led by Yahoo! and Doug
Cutting. It relies on an active community of contributors from all over the world for its success. With a
significant technology investment by Yahoo!, Apache Hadoop has become an enterprise-ready cloud computing
technology. It is becoming the industry de factor framework for Big Data processing.
Hadoop changes the economics and the dynamics of large-scale computing. Its impact can be boiled down to
four salient characteristics. Hadoop enables scalable, cost-effective, flexible, fault-tolerant solutions.
Exploring Hadoop features:
Apache Hadoop has two main features:
 HDFS (Hadoop Distributed File System)
 MapReduce
Techniques for Analyzing Big Data:
When you use SQL queries to look up financial numbers or OLAP tools to generate sales forecasts, you
generally know what kind of data you have and what it can tell you. Revenue, geography and time all relate to
each other in predictable ways. You don’t necessarily know what the answers are but you do know how the
various elements of the data set relate to each other. BI users often run standard reports from structured
databases that have been carefully modeled to leverage these relationships.

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Big data analysis involves making “sense” out of large volumes of varied data that in its raw form lacks a data
model to define what each element means in the context of the others. There are several new issues you should
consider as you embark on this new type of analysis:
Discovery – In many cases you don’t really know what you have and how different data sets relate to each
other. You must figure it out through a process of exploration and discovery.
 Iteration – Because the actual relationships are not always known in advance, uncovering insight is often
an iterative process as you find the answers that you seek. The nature of iteration is that it sometimes
leads you down a path that turns out to be a dead end. That’s okay experimentation is part of the process.
Many analysts and industry experts suggest that you start with small, well-defined projects, learn from
each iteration, and gradually move on to the next idea or field of inquiry.
 Flexible Capacity – Because of the iterative nature of big data analysis, be prepared to spend more time
and utilize more resources to solve problems.
 Mining and Predicting – Big data analysis is not black and white. You don’t always know how the
various data elements relate to each other. As you mine the data to discover patterns and relationships,
predictive analytics can yield the insights that you seek.
 Decision Management – Consider the transaction volume and velocity. If you are using big data
analytics to drive many operational decisions (such as personalizing a web site or prompting call center
agents about the habits and activities of consumers) then you need to consider how to automate and
optimize the implementation of all those actions.
Big Data Analysis Requirements:
Techniques for Analyzing Big Data, we discussed some of methods you can use to find meaning and discover
hidden relationships in big data. Here are three significant requirements for conducting these inquiries in an
expedient way:
1. Minimize data movement
2. Use existing skills
3. Attend to data security

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Fig:3 Big Data appliances on Social Networking sites and mobile devices.

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Chapter-5
Cloud Technologies
INTRODUCTION:
Business and IT perspective, Cloud and virtualization, Cloud services requirements ,cloud and dynamic
infrastructure, cloud computing characteristics, cloud adoption.
The impressive success of companies such as Google, Facebook with the use of Cloud Computing has made
companies to think towards making similar services and user experience to their users - employees, partners
and customers.
There are certain services and models working behind the scene making the cloud computing feasible and
accessible to end users.
Following are the working models for cloud computing:
 Deployment Models
 Service Models
DEPLOYMENT MODELS:
Deployment models define the type of access to the cloud, i.e., how the cloud is located? Cloud can have any of
the four types of access: Public, Private, Hybrid and Community.
PUBLIC CLOUD
The Public Cloud allows systems and services to be easily accessible to the general public. Public cloud may
be less secure because of its openness, e.g., e-mail.
PRIVATE CLOUD
The Private Cloud allows systems and services to be accessible within an organization. It offers increased
security because of its private nature.
COMMUNITY CLOUD
The Community Cloud allows systems and services to be accessible by group of organizations.

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HYBRID CLOUD
The Hybrid Cloud is mixture of public and private cloud. However, the critical activities are performed using
private cloud while the non-critical activities are performed using public cloud.
SERVICE MODELS:
Service Models are the reference models on which the Cloud Computing is based. These can be categorized
into three basic service models as listed below:
1. Infrastructure as a Service (IaaS)
2. Platform as a Service (PaaS)
3. Software as a Service (SaaS)
There are many other service models all of which can take the form like XaaS, i.e., Anything as a Service.
This can be Network as a Service, Business as a Service, Identity as a Service, Database as a Service or
Strategy as a Service. The Infrastructure as a Service (IaaS) is the most basic level of service. Each of the
service models make use of the underlying service model, i.e., each inherits the security and management
mechanism from the underlying model, as shown in the following diagram:
Fig 4:XaaS development

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CHAPTER-6
SMAC ARCHITECTURE
Social, Mobility, Analytics and Cloud, abbreviated SMAC, are separate platforms with technologies
thatevolved during last few years and have shown enormous enhancement. Instead of treating these four
components separately, current corporate organizations have started treating them integrally. This is the new
integrated IT model making corporate world more connected, collaborative, productive and real-time. In the
current IT scenario, in terms of business productivity, the number of computers is rapidly increasing and is on
its way to 100 billion and data volume is mounting to around 35,000 exabytes which is more than 600 times the
data under management at the end of internet era before 2012. Integration of Social, Mobile, Analytics and
Cloud presents a prospect for business sectors as well as for government to increase their revenues by mounting
into mega volume margin instead of traditional IT business or e-governance. These four key technologies are
working in combination of each other and bring revolutionary changes in terms of consumer satisfaction of any
business sector.
Social, Mobile, Analytics and Cloud (SMAC) are well built platforms indicating development in which business
ecosystems are more digitized and flattering where information content accounts for a growing proportion of
any product and service’s entire value. In SMAC, each and every technology is having its own impact and that
is complimentary for task completion. People are using more information from Cloud. The Cloud access is
available to people by Mobile devices. An actionable sense of the data available in cloud can be done by
Analytics. Finally, for finding colleagues to collaborate with and co-create social media is available throughout
for help. The broad concept of SMAC is helpful for understanding impact of technology integration from
strategic viewpoint which is deliberated.
COMPONENTS OF SMAC:

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ARCHITECTURE OF SMAC:
Fig:5 SMAC Architecture
SMAC Effect:
In all Industries across the business landscape, the SMAC Stack™ is eroding the century-old blueprint of value
chains and spawning new, highly distributed, virtualized business models. The power of this technology
platform is in treating it as a stack, for its components have a multiplying effect when they work in
combination.

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Chapter-7
Applications
 Mobile Banking,
 E-governance,
 Market Intelligence,
 Business Productivity,
 Online marketing,
 Social Media,
 E-store
 Social networks banking like twitter,facebook,
 Machine Behaviour (machine Learning),
 Automobiles (Bikes, cars,etc, Implemented on Mahindra Earth Master by Tech Mahindra),
 Desktops/PC’s for Technical assistance,

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CONCLUSION
Every application and every human being is, or will be touched by, at-least one technology of SMAC stack.
Business leaders need to be cautious of advancement in this stack and should not miss the bandwagon, as this
technology stack in coming years will transform the way they run their businesses. So, this stack has high
potential to disrupt their IT landscape. They should carefully evaluate and analyze the impact from each of these
technologies on their software applications and adopt accordingly. However, there is no immediate adoption
need to act in haste, but a careful impact analysis is required. Especially for enterprise applications, the impact
of this entire stack is not high - only Cloud and Analytics will have some impact.

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REFERENCES
1.Figures from Google Internet open sources.
2.Social Networking from danah m. boyd University of Berkley.
3.Mobile Technology by Pact.Inc.
4.Big Data Analytics by Vignesh Prajapapthi-open source.
5.Big Data Analytics from Oracle Analytics-open source.
6.Cloud Computing from www.Tutorials point.com.
7.Introduction of SMAC from Dinodia capital Advisors.
8.SMAC from www.csi-org.com. ISSN 0970-647X,volume:38,issue 07,October 2014.

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