Big Data Processing Of Cloud Computing

As the camera pans across the ocean shore, the sky blue waves crash against the massive rocks as the scene pans over to a close up shot of Dominick Cobb’s (Leonardo DiCaprio) hair saturated with water and his eyes as blue as the ocean waves. The camera tilts as Cobb’s raises his head and his glassy eyes appears to be gazing at something that does not seem to be in existence as the scene reveals a shallow focus view of two kids playing on the beach. This shot’s blurry appearance seem to be Cobb’s perception. In the movie Inception (Christopher Nolan, 2010), the use of point of view shots convey the notion that our perception is our reality. In the film, Nolan uses point of view shots to dissect Cobb’s thoughts in a visual manner specifically

The big data analytics deals with a large amount of data to work with and also the processing techniques to handle and manage large number of records with many attributes. The combination of big data and computing power with statistical analysis allows the designers to explore new behavioral data throughout the day at various websites. It represents a database that can’t be processed and managed by current data mining techniques due to large size and complexity of data. Big data analytic includes the representation of data in a suitable form and make use of data mining to extract useful information from these large dataset or stream of data. As stated above the big data analytics has recently emerged as a very popular research and practical-oriented framework that implements i) data mining, ii) predictive analysis forecasting, iii) text mining, iv) virtualization, v) optimization, vi) data security, vii) virtualization tools for processing very large data sets. In the implementation of big data applications, new data mining techniques and virtualization are required to be implemented due to the volume, variability, forms and velocity of the data to be processed. A set of machine learning techniques based on statistical analysis and neural networking technology for big data is still evolving but it shows a great potential for solving a big data business problems. Further, a new concept of in-memory database for enhancing the speed for analytic processing is further helping

The author points out that although there are existing algorithms and tools available to handle Big Data, they are not sufficient as the volume of data is exponentially increasing every day. To show the usefulness of Big Data mining, the author highlighted the work done by United Nations. In order to further enhance the reader’s perspective, the author provided research work of various professionals to educate its readers about the most recent updates in Big Data mining field. The author further describes the controversies surrounding Big Data. The author has first provided the context and exigence by elaborating on why we need new algorithm and tools to explore the Big Data. The author used the strategy of highlighting the logos by mentioning the research work of different industry professionals, workshops conducted on Big Data and was able to appeal to connect to the reader’s ethos. The author also used pathos by urging the budding Big Data researchers to further dig deep into the topic and explore this area

Cloud Computing and Big Data technologies can effectively be used to change the way in which businesses operate. Companies operating in fields such as financial, educational, technological and many more, have adopted the use of these technologies or are exploring the possibilities to become part of this new trend, as the benefits are proven to be a tremendous success. Not only for the increase in profits, but these technologies have a positive impact in many areas of a business. The success of Cloud Computing and Big Data has transformed businesses around the globe and “forced” them to spend billions on cloud computing services. The popularity of these technologies is rapidly growing and they are now accessible not only to giant companies that would spend limitless amount of money in order to have the best technology available, but also to small and medium size companies with a less significant budget.

Till the last Century, business strategy was an outcome of low transactional costs and monopoly of propriety products. The strategies based on these two fundamentals worked well because reducing transactional cost lowered the operating cost of business whereas the propriety product and experience retained an edge over the competitors. The evolution of Big Data not as a technology but in essence the 3Vs(Volume, Veracity and Velocity) coupled with reduction in transactional cost has changed the way businesses need to strategize. This data although wide-spread across the globe is still connected. Thus its potential need to be harnessed to produce much more focused and accurate analysis. The focus has also shifted from mundane traditional fault reporting systems to intelligent systems which provide analytics. The need is to use analytics to proactively identify hardware faults and configuration issues before they occur and cause incidents. Storage systems are an important component in this setup. IT storage systems need to be enabled with powerful management capabilities to improve the efficiency amid this rapid growth. Cloud storage services provide one such capability and can be used as a possible solution. The Cloud storage services provide the following features as defined by National Institute of Standards and Technology: on-demand self-service, resource pooling, rapid elasticity, broad network access and

Big Data Now : 2016 Edition is a collection of big data and data science blogs and excerpts written by various O’reilly authors. It brings forward the knowledge of executing big data project and creating scalable solutions.

The main driving forces of cloud data storage are reputable companies such as Amazon and Google building comprehensive computing infrastructures (Google, 2009). These infrastructures are removing the complexity of in-house data storage and ultimately reducing costs of limited networked data centres (Hitachi, 2010). The traditional inefficient model of purchasing servers every time you need to accommodate for high use or growth is now being replaced by internet based systems that replicate your data centres but without the big overheads (Google, 2009). This flexibility assists in the ever changing business world and its continuous improvement initiatives to remove waste, improve efficiency, and ultimately reduce costs. Another key driving

As there is a rise in data volumes, the manageability of data and storing these huge volumes of data became a cause of concern to most of the organizations. It was during this period when Number of SQL or more popularly NoSQL was introduced, to process these large amounts of data efficiently and effectively. For this purpose, various Data Store categories were developed, based on the different data models. Some of the categories are:

Big data has received popularity as datasets which are large that cannot be easily managed by the traditional relational databases. Big data deals with high volume, variety and velocity of data. However, if traditional relational database schemas are applied to big data, the large volume of datasets cannot be processed and managed by these traditional techniques. The notable solution to manage and process the data is MapReduce framework. MapReduce is programming model which programming model where a large data sets is divided into small data sets; these small data sets are processed in parallel by nodes(computers). This is called as batch processing, where each worker node applies map function and reduce function to the associated data to

NoSQL is able to address the massive traffic loads experienced by database servers at corporations that specialize in data processing like Google, Facebook and Amazon. NoSQL technologies can provide near constant availability, massive user concurrency and lightning fast responses. There are four primary NoSQL database implementation types being used today: document based, wide column (or columnar), key-value and graph. The different properties of SQL and NoSQL databases will be examined and an overview of each NoSQL implementation type along with an example will be given.

With the fast computers and signal processors available in the 2000s, cloud computing become the most common form of data storage and generally, is used because it is not only the most versatile method, but also the cheapest.

In this proposed method, first we will be analyzing the performance of data processing individually on relational databases and Hadoop framework by taking a collection of sample datasets. After evaluating the performance of each system, we will be working on a new method of data processing by combinedly using both the computational powers of RDBMS and Hadoop frameworks. We will be using same experimental setup and configurations for analyzing data.

‘Big Data’ is the application of specialized techniques and technologies to process very large sets of data. These data sets are often so large and complex that it becomes difficult to process using on-hand database management tools. There are several techniques which are widely used in implementation of Big Data.

Abstract— Big data is a significant subject in modern times with the rapid advancement of new technologies for example, smartphones, pc/laptops, game consoles, that all in some way store information. Big companies require a place to not only store all the data that is coming in but to also analyze it for specific purposes and at the fastest speed manageable. There are many different providers out there who provide this service, this paper will talk about one way the company Google handles data using their own special made platform.

backing up their devices once or twice a week is good for their device. The reason