Big Data Technologies and Cloud Computing (PDF) | SciTech ConnectClick here to sign up for our email list and get notified of future posts. The advancement of technology has allowed companies to reap the benefits of streamlined processes and cost-efficient operations. But the one thing that has become a game changer for businesses of all sizes is the availability of data from every source imaginable — social media, sensors, business applications, and many more. These large stores of data that bombard companies day in and day out is collectively known as big data. Most have heard of it, many aim to maximize its potential to propel their business forward, and yet, only few have truly succeeded in doing so. At the same time, enterprises have adopted cloud computing to improve their IT operations and develop better software, faster.
Difference Between Cloud Computing and Big Data
Big data and cloud computing are the trending terms in the information technology IT world. The terms have also grown popular among business owners who are willing to take advantage of technologies to expand their businesses. However, big data and cloud computing are also some of the most confused terms in IT. Big data is not only a term used to refer to big volume of data, but also used to mean a refreshing way of gathering, storing, organising and analysing numerous types of data. Big data is can be explained as huge volume data that are analysed with computers for identifying trends, patterns and similarities. It is a terminology that explains large quantity of data structured, semi-structured and unstructured that can be processed for valuable information. When talking about big data, there are key characteristics that are combined together that makes it up.
Computer Science. By: Stephen Fiedler , Posted on: November 13, Due to bottlenecks such as poor scalability, difficulties in installation and maintenance, fault tolerance and low performance in traditional information technology framework, we need to leverage cloud computing techniques and solutions to deal with big data problems. Cloud computing and big data are complementary to each other and have inherent connection of dialectical unity. In this chapter, we focus on discussing the development and pivotal technologies of big data, providing a comprehensive description of big data from several perspectives, including the development of big data, the current data-burst situation, the relationship between big data and Cloud computing, and big data technologies. We also discuss related researches in the end.
Big data simply represents huge sets of data, both structured and unstructured, that can be further processed to extract information. Huge volumes of data are being generated over the internet every second and one machine is not enough to handle all the data which comes in all kinds of formats. It provides keen insights to the prospective business owners who would then gather, store, and organize the data for further analysis. Storing the data would have been a problem in the earlier days, but thanks to the new technologies, organizing data has become so much easier, especially with computers doing all the hard work. A few important characteristics define the big data that can lead to strategic business moves. These features are volume, variety and velocity of data.
Data is everywhere. In fact, the amount of digital data that exists is growing at a rapid rate, doubling every two years, and changing the way we live. According to IBM , 2. An article by Forbes states that Data is growing faster than ever before and by the year , about 1. Which makes it extremely important to know the basics of the field at least.
Data with many cases rows offer greater statistical power , while data with higher complexity more attributes or columns may lead to a higher false discovery rate. Big data was originally associated with three key concepts: volume , variety , and velocity. Therefore, big data often includes data with sizes that exceed the capacity of traditional usual software to process within an acceptable time  and value. Current usage of the term big data tends to refer to the use of predictive analytics , user behavior analytics , or certain other advanced data analytics methods that extract value from data, and seldom to a particular size of data set. Scientists encounter limitations in e-Science work, including meteorology , genomics ,  connectomics , complex physics simulations, biology and environmental research. Data sets grow rapidly, in part because they are increasingly gathered by cheap and numerous information-sensing Internet of things devices such as mobile devices , aerial remote sensing , software logs, cameras , microphones, radio-frequency identification RFID readers and wireless sensor networks. By , IDC predicts there will be zettabytes of data.