Cloud computing offers platform independency, as the software is not required to be installed locally on the PC. Hence, the Cloud Computing is making our business applications mobile and collaborative.

What is Cloud Computing? Cloud computing is not a new concept. We have been using Cloud

Computing for ages, in one form or other. In simple words, you can presume Cloud to be a very large server on which different services and data are stored and you access all those for your work. The software and data that you access for your work doesn’t exist on your computer instead it’s on the server. This concept of using services not stored on your system is called Cloud Computing

• Cloud Computing is not an object in itself, rather it comprises services and database that are accessed via web or any private network.

• Key Cloud Computing providers: IBM, HP, Google, Microsoft, Amazon Web Services, Salesforce.com, NetSuite, VMware etc.

• Examples of Cloud Computing services includes Google Docs, Office 365, DropBox, SkyDrive etc.

Types of Cloud Computing

• Cloud Computing is composed of three service models and four deployment methods.

• Cloud Computing Service Models

• 1. Infrastructure as a Service (IaaS)2. Platform as a Service (PaaS)3. Software as a Service (SaaS)

Infrastructure as a Service (IaaS):  The IaaS layer offers storage and infrastructure resources that is needed to deliver the Cloud services. It only comprises of the infrastructure or physical resource. Top IaaS Cloud Computing Companies: Amazon (EC2), Rackspace, GoGrid, Microsoft, Terremark and Google.

Platform as a Service (PaaS):  PaaS provides the combination of both, infrastructure and application. Hence, organisations using PaaS don’t have to worry for infrastructure nor for services. Top PaaS Cloud Computing Companies: Salesforce.com, Google, Concur Technologies, Ariba, Unisys and Cisco.

Software as a Service (SaaS):  In the SaaS layer, the Cloud service provider hosts the software upon their servers. It can be defined as a in model in which applications and softwares are hosted upon the server and made available to customers over a network. Top SaaS Cloud Computing Companies: Amazon Web Services, AppScale, CA Technologies, Engine Yard, Salesforce and Windows Azure.

Cloud Computing Deployment Models

• 1. Private Cloud

• 2. Public Cloud

• 3. Hybrid Cloud

• 4. Community Cloud

Cloud Computing Deployment Models• Private Cloud:  The cloud infrastructure is

operated solely for an organisation. In simple words we can say that such cloud models are dedicated to a third party who wish to use. It may be managed by the Cloud Computing provider or any other third party.

• Public Cloud: The cloud infrastructure is made available to the general public or a large industry group and is owned by the Cloud providers.

• Hybrid Cloud: It’s a combination of two or more clouds (private, community or public).

• Community Cloud: This cloud infrastructure is shared by several organisations.

Benefits of Cloud Computing• One can access applications as

utilities, over the Internet.

• One can manipulate and configure the applications online at any time.

• It does not require to install a software to access or manipulate cloud application.

Benefits• Cloud Computing offers online development and

deployment tools, programming runtime environment through PaaS model.

• Cloud resources are available over the network in a manner that provide platform independent access to any type of clients.

• Cloud Computing offers on-demand self-service. The resources can be used without interaction with cloud service provider.

• Cloud Computing is highly cost effective because it operates at high efficiency with optimum utilization. It just requires an Internet connection

Benefits

Risks related to Cloud Computing

Security and PrivacyIt is the biggest concern about cloud computing. Since data management and infrastructure management in cloud is provided by third-party, it is always a risk to handover the sensitive information to cloud service providers.

Although the cloud computing vendors ensure highly secured password protected accounts, any sign of security breach may result in loss of customers and businesses

Lock InIt is very difficult for the customers to switch from one Cloud Service Provider (CSP) to another. It results in dependency on a particular CSP for service.Isolation FailureThis risk involves the failure of isolation mechanism that separates storage, memory, and routing between the different tenants.Management Interface CompromiseIn case of public cloud provider, the customer management interfaces are accessible through the Internet.Insecure or Incomplete Data Deletion

Data AnalyticsBig Data

• Big data can come from several different sources. Now, one way to think about it is whether the data was produced by a human or whether it was produced by a machine. We humans generate a lot of data whether we mean to or not.

• Intentional data is data that you know you are creating, so for instance, if you take photos, videos, or record audio or put text on a social network, you know you're doing it. You can also click "like" if you're on Facebook.

Big Data

 When you do web searches, a record of the web pages that you have viewed are bookmarked. Your emails and your text messages. Your cell phone calls. If you read an eBook, the highlights, the notes in the bookmarks and online purchases. All of these are kinds of data that do not exist until the person deliberately makes them happen, so these are records of human actionsIN addition to these intentional pieces of information, there's also meta data. Now, meta data is data about data.meta data, and can be enormous, sometimes larger than the actual piece of data you created, and most significantly for the big data world, meta data, because it's computer generated, is already machine readable and searchable

Big Data

• For instance, if you take a picture with your phone, you not only get the picture, you also get what's called the EXIF data. That stands for exchangeable image file format, and this is the meta data that comes from a picture on an iPhone. Now, aside from the name of the file which you see at the top left and that it's 3.1 megabytes, and the time that it was taken, near the bottom on the right side, you see the GPS altitude, latitude, longitude and position, or partway up from that, you'll even see the GPS image direction.

Big Data

Several times, the meta data in this case is several times greater than the actual content that it's about. This is one of the most interesting things, and this is why Twitter in particular is a very rich data set for people who are doing marketing research or social connection research. Anyhow, the point of this is that these are all sources of big data, and one of the interesting things about it is that the meta data in particular does not have to be processed. It's already computer readable. It's searchable. It's mindable, and you can start to get information about it immediately to reach your big data for analysis purposes.

Big Data Applications

• Big data analysis helped in parts, responsible for the BJP and its allies to win Indian General Election 2014

• The Indian Government utilises numerous techniques to ascertain how the Indian electorate is responding to government action, as well as ideas for policy augmentation

• Based on TCS 2013 Global Trend Study, improvements in supply planning and product quality provide the greatest benefit of big data for manufacturing.Big data provides an infrastructure for transparency in manufacturing industry, which is the ability to unravel uncertainties such as inconsistent component performance and availability.

Contd-

Predictive manufacturing as an applicable approach toward near-zero downtime and transparency requires vast amount of data and advanced prediction tools for a systematic process of data into useful information. A conceptual framework of predictive manufacturing begins with data acquisition where different type of sensory data is available to acquire such as acoustics, vibration, pressure, current, voltage and controller data. Vast amount of sensory data in addition to historical data construct the big data in manufacturing. The generated big data acts as the input into predictive tools and preventive strategies such as Prognostics and Health Management

Contd- To understand how the media utilises Big Data, it is first

necessary to provide some context into the mechanism used for media process. It has been suggested by Nick Couldry and Joseph Turow that practitioners in Media and Advertising approach big data as many actionable points of information about millions of individuals. The industry appears to be moving away from the traditional approach of using specific media environments such as newspapers, magazines, or television shows and instead tap into consumers with technologies that reach targeted people at optimal times in optimal locations. The ultimate aim is to serve, or convey, a message or content that is (statistically speaking) in line with the consumers mindset. For example, publishing environments are increasingly tailoring messages (advertisements) and content (articles) to appeal to consumers that have been exclusively gleaned through various data-mining activities.

Big Data Characteristics

Velocity - velocity: this is when data is coming in very fast. In conventional scientific research, it could take months to gather data from 100 cases, weeks to analyze the data, and years to get that research published. Not only is this kind of data time consuming to gather, it's generally static once it's entered, that is, it doesn't change.

At the other end of the scale, if you're interested in using data from a social media platform, like Twitter, you may have to deal with the so-called "fire hose". In fact, right now they're processing about 6,000 tweets globally per second. That works out to 500,000,000 tweets per day and about 200,000,000,000 tweets per year.

Big Data Characteristics

 

Now, this kind of constant influx of data, better known as streaming data, presents special challenges for analysis, because the data set, itself, is a moving target. If you're accustomed to working with static data sets, in a program like SPSS or R, the demands and complexities of streaming data can be very daunting, to say the least.

Variety And now we get to the second aspect of big data, variety. What we mean here is that it's not just the rows and columns of a nicely formatted data set in a spread sheet.Instead you can have many data sheets in many different formats. You can have unstructured text, like books and blog posts and comments on news articles and tweets. One researcher has estimated that 80 percent of enterprise data may be unstructured, so it's the majority as the common case. This can also include photos and videos and audio. Similarly, data sets that include things like networked graph data, that's social connections data. Or if you're dealing with data sets in what is called noSQL databases, so you may have graphs of social connections. you may have hierarchical structures and documents. Any number of data formats that don't fit well into the rows and columns of a conventional relational database or a spreadsheet, then you can have some very serious analytical challenges. 

Big Data Characteristics• Instead you can have many data sheets in many

different formats. You can have unstructured text, like books and blog posts and comments on news articles and tweets. One researcher has estimated that 80 percent of enterprise data may be unstructured, so it's the majority as the common case.

•  This can also include photos and videos and audio. Similarly, data sets that include things like networked graph data, that's social connections data. Or if you're dealing with data sets in what is called noSQL databases, so you may have graphs of social connections. you may have hierarchical structures and documents. Any number of data formats that don't fit well into the rows and columns of a conventional relational database or a spreadsheet, then you can have some very serious analytical challenges.

Big Data Characteristics

• Volume•  In its simplest possible definition, big data is data that's

just too big to work on your computer. Obviously this is a relative definition.

• In Excel the maximum number of rows that you could have in a single spreadsheet has changed over time.Previously it was 65,000. Now it's over a million, which seems like a lot, but if you're logging internet activity where something can occur hundreds or thousands of times per second, you'll reach your million rows very, very quickly. On the other hand, if you're looking at photos or video and youneed to have all of the information in memory at once, you have an entirely different issue. Even my iPhone takes photos at two or three megabytes per photo and video at about 18 megabytes per minute, or one gigabyte per hour

Types of Data

• IoT: Internet of Things• Now, it's estimated that by 2020, which is only a few

years from now, as many as 30 billion uniquely identifiable devices may be connected to the internet. This actually requires a major change in how the internet works it's addressing system for all of these things to fit, but basically, everything will have a chip and it will be connected to the internet and they'll be talking to each other, sharing information.

•  So when you hear people talk about smart sensors, in your home or in your city or on your air conditioning or the smart home, which knows when to turn lights on or change the temperature, or the smart grids where the city generates and sends out the electricity or the smart city itself which coordinates all of the traffic and the

Types of Data

utilities as a way of being more efficient and more economical in providing better service. All of this would be enabled by small objects communicating one with another in the internet of things, they communicate directly and not with a human an intermediary. Some of the uses for this can include putting sensors on production lines to monitor systems for when they need maintenance, or the smart meters on utility systems to shut them off at peak times, if they can do it without interrupting service.

Types of Data

• Structured data•  Data is said to be structured when it's placed in a file with

fixed fields or variables. The most familiar example of this kind of structured database is a spreadsheet. Where every column is a variable and every row is a case or observation. In the business world however, large data sets are usually stored in databases. Relational databases to be specific, which share some characteristics with spreadsheets such as rows and columns, but allow for much larger data sets, more flexibility, and more constancy. A recent survey of database users found that nearly 80 percent use some form of relational database with Microsoft SQL server, MySQL, and Oracle as the most common options.

Types of Data

Unstructured DataIf you were to write this in a report, you might say something like, "Two prominent teams in Asia India and Sri Lanka."Now, while this data is easy for a person to understand, it's much more difficult for a machine to understand. It's not easy to sort this kind of data. It's hard to rearrange it. It's hard to count the values, and it's hard to add more observations. So this is an example of unstructured data, that's data that's not in fixed fields and text documents, presentations, images, video, audio, PDFs, what have you, all go into this. And it may be the majority of data in business settings. It has been estimated anywhere from about 45 percent up to as much as 80 percent of business data may be unstructured. It's a little hard to deal with. You may have to convert it to text and then use a text-mining program to try to get structure out of the sentences of data, but that's difficult, and it's time consuming to do. On the other hand, data doesn’t have to be either structured in a spreadsheet or unstructured in text. A third option is available, and that's semi-structured data.

Types of Data

• The next thing we want to talk about, once we've discussed unstructured and semi-structured data, is the databases that you can store the information in, as opposed to the SQL databases that are used for structured data with rows and columns. Semi-structured and unstructured data usually go into go into what are called NoSQL databases.

•  That used to mean not SQL, but now it means not only SQL, because NoSQL databases are extremely flexible and can handle a wide range of data formats. So most of them use a semi-structured format. For instance, the most common NoSQL database is MongoDB that uses JSON. It's nice because it's a flexible structure, and for certain tasks, a NoSQL database can be much faster

Big Data AnalyticsMonitoring and anomaly detection -- Big data can be helpful for letting people know when unusual things happen or possibly, when they're about to happen. These kinds of notifications can fall into two general categories, although there are other systems for describing notifications. They are monitoring and anomaly detection. Monitoring can be very helpful when you know what you're looking for and you need a notification when that thing occurs. It detects when a specific event occurs. So you need to be able to specify the criterion in advance. For example, a manufacturer needs to know when one of their machines needmaintenance, so they may look at temperatures. They may look at vibration levels. They may look at a number of factors that let them know that breakdown is imminent. Take care of it now.

Big Data Analytics

• Anomaly detection, on the other hand, can describe a situation in which the user wants to know when something unusual happens. They're looking for a notification of unusual activity without necessarily knowing in advance what that something might be. As a result, it needs to be based on flexible criteria, and it says, "Let me know when something "that is out of the ordinary happens, "maybe not just on one factor, "but, like, on a combination of several different factors." And the flexible criteria usually exists to draw a person's attention to something.

•  

Big Data Analytics

anomaly detection can notice patterns that, for instance, may be too far spread apart in big data or may be too fine for humans to notice on their own. What big data allows here is not that these things can occur, because monitoring anomaly detection have occurred for forever. Now both of these approaches, monitoring and anomaly detection, are common practices and predate not just big data but computers as well. What big data adds to them though is the possibility to watch for extremely rare events or combinations of factors. So, for instance, if you have an event that's a one-in-a-million. It only occurs one time out of a million observations, that could be really hard to spot if you're doing it by hand or if you're doing it, for instance, even a hundred cases at a time, but if you have ten billion cases that you're sorting through, this one in a million event is going to occur 10,000 times. And suddenly, that's not a small a number.  

Big Data Analytics

• Data mining and text analytics•  One of the most powerful and common applications of

big data is in Data Mining and it's close cousin Text Analytics.

• Data Mining covers a large and diverse field of activities but the most basic idea is this, use statistical procedures to find unexpected patterns in data. Those patterns might include unexpected associations between variables or people who cluster together in unanticipated ways. For example, managers in a supermarket team might find that people who visit their stores in a particular region, on a particular night of the week are generally different from people who come at other times and places.

Big Data Analytics

Perhaps the most common application of this kind of Data Mining is with online advertising because the data base is so large and because it's so easy to adapt the results for each specific viewer. In fact, that's one of the biggest promises of Data Mining, the ability to tailor services to the preferences and behaviors of each individual person once enough data has been gathered. Text Analytics is closely related to the standard kind of Data Mining that deals exclusively with numbers, however Text Analytics is sufficiently distinct to be it's own field. The goal here is to take the actual content of Text data, such as tweets or customer reviews and find meaning and pattern in the words. .

Big Data Analytics

• Perhaps in Text Analytics, the most common task is probably what they call sentiment analysis, or determining how people feel about something. That makes sense if you're thinking about advertising or marketing point of view, you definitely wantto know if people feel good or bad about your particular product.

•  The most basic task in sentiment analysis is determining if a person's feelings are positive or negative. This is referred to as Polarity, in the Text Analytics world

Big Data Analytics

Predictive analytics - Predictive analytics is the crystal ball of big data. That is, it represents a range of techniques that are adapted to work with big data to try to predict future events based on past observations. And while people have been trying to predict the future ever since there have been people, the raw resources of big data and the sophistication of modern predictive modeling have fundamentally changed the way that we look into the future.

Big Data Analytics

Predictive analytics is an enormous area of interest because, especially if you're in the business world, trying to predict what is going to happen and having a little bit of foreknowledge can get you a huge competitive advantage. It's an area of incredible growth, and it really is one of the most fascinating things about statistics, because there's always a very clear criterion, which is something that's often lacking. You can tell, if you wait just a little bit, you can tell whether your model is good or whether it wasn't, and the progress in the field makes it possible to learn more and more, and especially with the raw material from big data, there's so much more to work with to build more new ones,more refined models, and to get better predictive abilities for more competitive advantage.

Big Data Analytics

Big Data Visualization - Up to this point, we've been talking about big data, and the things that the computers are able to do for the humans. On the other hand, it turns out there are certain things that humans still do better than computers, and visualization is one of them. Humans are visual animals. We work on sight, and we get a huge amount of information that way. Computers are very good at spotting certain patterns. They're also very good at calculating predictive models and doing data mining in a way that humans would have a hard time doing in a thousand lifetimes. 

Big Data Analytics

But, humans perceive and interpret patterns much better than computers do, and so human vision still plays an important role in big data. Humans can see the patterns, and they can see the exceptions to the patterns or the anomalies very quickly. They can also see those patterns across multiple variables and groups. They're also much better at interpreting the content of images than computers are.