Cloud computing is the act of treating data storage and computing data as commodities by the use of standardized technologies. Big data is the information involved while cloud computing is the process of getting or storing the information. The term “Big data” commonly refers to high amounts of data and technology (Gandomi 2015). Cloud computing is merely storing information or data to a remote server.
The advantages of cloud computing include connectivity, offsite manage, lower upfront cost, storage immortality, rapid deployment, speed, and scalability. They are however disadvantages including security, rate, lifetime costs, compliance, and noisy neighbors.
Most people prefer cloud computing as it is a better alternative form of storage of information as compared to more traditional means. Storing data to a computer’s hard drive as is traditionally done can leave the info very vulnerable to hacking. Hacking has become an everyday occurrence with hackers growing more and more elusive by the day. Big organizations such as the F.B.I. That has a large team of internet security experts has fallen prey to hackers putting personal computers that have taken no measures to protect themselves from such attacks at significant risk of having their information stolen.
This and also the danger of a computer being physically stolen makes backing up your data or entirely storing in the cloud data storage system very appealing. For those who have no external backup systems in place, cloud storage provides a similar service without incurring the cost of purchasing an external backup system.
When an individual selects a storage system for his information, security, and ease of accessibility is primarily the incentive. One needs to be guaranteed that come rain or shine, and his knowledge is entirely safe and secure to any form of hacking. This is why cloud data storage is very appealing as it is not only secure and reliable but a piece of individual information is stored far away from his computer and if so desired, can be retrieved from another computer. Due to so many advantages, it is no surprise that cloud computing is now faced with big data due to the number of users who prefer cloud computing.
The growth of Big Data Analysis.
Big data analytics is the process of observing and examining large and diverse data sets often done by brands, companies, or organizations to uncover information that could be valuable to them including market trends, hidden patterns, and unknown correlations and consumer preferences (Sivarajah 2017). For the heavy analysis of big data, data analytics techniques and technologies are employed as the data is pervasive to be comprehensibly analyzed by any other means such as traditional means.
The emergence of cloud computing greatly influenced Big Data analysis as companies were now able to easily access large pools of data from all over the world and could also monitor competition as well us directly determine the trends and mannerism of customers and specific clientele in a way that was not possible before. It has become very crucial to companies all over the world with many having a team to handle the task.
Tools Used For Big Data Analytics
For this burdensome task, some professional tools and software’s are used to complete the work. The devices include R programming. R is currently the leading tool in big data analytics, and it is widely used for data modeling and statistics. The reason for its popularity among large organizations is because of its accessibility, outcome, and performance. It runs on a wide range of operating systems also a reason for its success (Sivarajah 2017). Currently, it is compatible with Windows, MacOS, and Linux
Tableau public is also a widely used tool for data analytics. What makes it viable is unlike R programming, Tableau public is free. It is also very versatile as it connects any data desirable including web-based data, corporate data warehouse and Microsoft excel (Gandomi 2015). It has the capabilities to create data visualizations, dashboards, and maps with real-time updates being presented on the internet.
Python which is an open source scripting language supports both functional and structured programming models giving a user or corporate the ability to create its specific tool to data analysis according to its preferences. Another widely used tool is the SAS which is a programming language and environment used for data manipulation and big data analytics. It is not limited by any means and can collect data from many sources. It is also widely used for profiling target customers as it can also conduct its analysis on social media.
Apache Spark has also become a key player in big data analytics even though it was developed in the University of California. Its popularity is due to its speed as it is a fast large scale data processing engine. His speed is awe-inspiring as it executes applications in Hadoop clusters ten times faster on a disk and a hundred times faster in memory. It can be said to reduce the heavy work of data analytics by saving a significant amount of time as compared to other tools.
Microsoft Excel which is accessible to any person with a windows computer is also widely used especially in smaller firms and corporations. It becomes essential when analytics of clients internal data is required as it is the only tool that can do that making people who use other devices also use it for that specific function.
A rapid miner is also a favorite tool in the field of big data analytics, the reason to its extensive spread use is chiefly because it can incorporate any data resources made or derived at by any other tool. Data analysts and data scientists are fond of it because of this function that is not offered in any other data analyst tool.
Splunk, another favorite tool in big data analysis analyzes and searches the machine for any required data. QlikView is also an ideal tool that has unique features such as in-memory data processing and patented technology. Knime which was developed in 2004 has been successfully integrated into cloud computing and can perform very complex big data analysis.
Importance of Big Data Analysis
Big data analysis is an invaluable tool in today’s market space as it greatly determines and informs a corporation or firm on the current trends in its specific business atmosphere. The above mentioned big data analytics tools greatly help on telling a company’s data scientist or specialist in analyzing growing volumes of transaction data as well as other forms of data which are left untapped when using analytics programs and Business Intelligence.
Big data analysis is instrumental in creating new revenue for a given company as the big data analysis is a significant part of research and may mean the difference between success and failure of a company (Assunção 2015). New revenue is created by using analysis provided in narrowing down to a new market or clientele.
Big data analysis has without a doubt become the backbone of trade in the contemporary world. Entire businesses owe their success to Big Data Analysis as this has become a significant part in research and marketing being present in every modern day company (Assunção 2015). Information collected from big data analysis is just if not more valuable than frequent feedback as it collects targeted information from various sources in regards to its business and can analysis different customer behaviors from region to region.
Big data analysis is without a doubt the future of research and marketing with its use becoming more and more widespread with each passing day. It is safe to call it a revolution in the business landscape as it has effectively and nearly entirely revolutionized how companies, cooperation’s and firms conduct their research. Business today would be very different if there were no big data analysis tools. Most collaborations would have an exceedingly hard time in expanding their operations relying on traditional research means such as surveys and use of questionnaires.
Big data analysis is now without a doubt a major player in the field of research, and due to ever-present technological advancements in the area, big data analysis will only continue to improve and become faster, more integrated and accessible to nearly if not all businesses in the modern world.
Sivarajah, U., Kamal, M. M., Irani, Z., & Weerakkody, V. (2017). Critical analysis of Big Data challenges and analytical methods. Journal of Business Research, 70, 263-286.
Gandomi, A., & Haider, M. (2015). Beyond the hype: Big data concepts, methods, and analytics. International Journal of Information Management, 35(2), 137-144.
Assunção, M. D., Calheiros, R. N., Bianchi, S., Netto, M. A., & Buyya, R. (2015). Big Data computing and clouds: Trends and future directions. Journal of Parallel and Distributed Computing, 79, 3-15.