Big Data Processing Of Cloud Computing

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

Big Data is an expansive phrase for data sets so called big, large or complex that they are very difficult to process using traditional data processing applications. Challenges include analysis, capture, curation, search, sharing, storage, transfer, visualization, and information privacy. In common usage, the term big data has largely come to refer simply to the use of predictive analytics. Big data is a set of techniques and technologies that need or require new forms of integration to expose large invisible values from large datasets that are diverse, complex, and of a massive scale. When big data is effectively and efficiently captured, processed, and analyzed, companies

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

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

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

Big data is a popular term used to describe the exponential growth and availability of data, both structured and unstructured. And big data may be as important to business – and society – as the Internet has become. Why? More data may lead to more accurate analyses. More accurate analyses may lead to more confident decision making. And better decisions can mean greater operational efficiencies.

The sudden emergence of cloud storage has taken over organizations by storm. A recent study that was conducted, concluded that, the number of organizations using cloud storage has almost doubled in the last few years alone. And in addition, the public cloud service market will exceed $244 billion by 2017 (Baiju, 2014). Using the cloud can provide many competitive advantages for a business. This includes; usability, bandwidth, disaster recovery, and cost savings.

Big data is certainly one of the biggest buzz phrases in it today. The term ’Big Data’ appeared for first time in 1998 in a Silicon Graphics (SGI) slide deck by John Mashey with the title of ”Big Data and the Next Wave of InfraStress” [9]. -Combined with virtualization and cloud computing, big data is a technological capability that will force data centers to significantly transform and evolve within the next five years. Similar to virtualization, big data infrastructure is unique and can create an architectural upheaval in the way systems, storage, and software infrastructure are connected and managed. Big data is an amalgam of large and varieties of data sets including structured data, semi structured data and unstructured data so it’s beyond the capability of traditional tools to capture, store, process and analysis of big data. It is true that big data have capability of unlocking new sources of development in many fields but at the same time researchers are being confronted challenges with big data. This paper reveals the various challenges faced with big data and opportunities realized with big data. Keywords: Big data, Challenges, Opportunities, Security Issues.

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.

To address the question of how and what techniques has been used to manages this big amount of data or in the field of Big Data, I review some research papers and review articles in the field of Big Data. This paper provides the synthesis of those papers which I found relevant to this field. This paper will focus on the following things:

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.

There are many fundamental issue areas that need to be addressed in dealing with big data: data acquisition, data storage, data transfer, data management, and data processing. Each of these issues represents a large set of technical research problems and challenges in its own right.

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.

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’ 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.