Big Data and Cloud Computing: Trends and Challenges

Big data is among the top important new technologies. Big Data are often used as an expression of the inability of traditional data structures to effectively handle the latest data sets. The four V’s of big data Volume, Velocity as well as variety and veracity make the management and analysis of data difficult for conventional data warehouses. It is crucial to think about large data as well as analytics in conjunction. “Big data” is the word used in reference to the current surge of various types of data that come from different sources. Analytics involves analyzing the data to identify fascinating and relevant patterns and trends that are used to make the decision-making process, improve processes, and even to drive innovative business models. Cloud computing appears to be the ideal platform to host big data-related workloads. 

However, processing large information in the cloud presents the challenge of balancing two design principles that are contradictory. Cloud computing is built on the notions of resource pooling and consolidation however, large data platforms (such such as Hadoop) have been built around the shared-nothing principle, in which each node is completely independent and self-sufficient. In integrating big data and cloud computing technology, businesses as well as educational institutes could have an advantage in the future.

The major participants of the Cloud computing market are Amazon Web Services (AWS), Microsoft Azure, Google Cloud Platform (GCP), IBM Cloud, VMware Cloud, Oracle Cloud and Alibaba Cloud. Based on Canalys study, a leading global market analyst firm for technology, AWS has a 32.3 percent market share with 16.9 percent for Microsoft Azure and 5.8 percent for GCP. Alibaba has recently emerged as a Chinese company that holds 4.9 percent of market, is likely to change the order of dominance over the next few years.

Cloud computing is increasingly important for not only software developers, but also in area that deals with big data analysis as well. cloud computing helps expand computing capabilities and the deployment of data solutions much more simple and, consequently, is useful for data scientists exploring large data sets.

Three largest cloud providers offers a suite of tools that are powerful designed for scientists working with data:

For AWS the most well-known tools comprise Redshift, EC2, EMR, S3, Data Pipeline and Database Migration Service. Clients are Standard Chartered Bank and S&P Global Ratings (financial services), Skyscanner (travel & hospitality), Nielsen (marketing & advertising), Royal Dutch Shell (energy) and The Guardian (media).

AWS remains the leader and has built on its initial mover advantage. The company began cloud computing at the beginning of 2006, more than seven years prior to when competitors entered the market. But, in terms of sales gains, GCP has experienced an increase of 88% from 2018 through 2019 quickly expanding its presence in the field of cloud computing.

Big data refers specifically to data that is massive in size and growing rapidly in relation to time. Big data can be defined as unstructured, structured and structured data, and semi-structured data. Big data is not processed and stored in conventional tools for managing data. It requires specific tools for managing big data. This refers to complicated and massive data sets that have five V’s: volume, velocity, veracity and Value of information assets. It encompasses data storage as well as data analysis, mining, and data visualization.

The volume of information and data gathered from media devices and mobile phones by companies is growing at a rapid pace and has nearly doubled every year. The sheer amount of data created is classified as unstructured or structured data that can’t be transferred into traditional database systems. The massive data needs to be processed to transform it into a tidy data sets that can be used for analysis. Engineering, finance, health care Ecommerce, and other scientific areas use this data to analyze and for taking decisions. The development of data science, cloud computing and data storage has enabled processing and storage of huge data [11. Cloud computing has brought about an increase in parallel processing access, scalability, accessibility data security as well as virtualization of resources and integration with data storage. Cloud computing has reduced the cost of infrastructure required to purchase equipment, facilities, utilities or even building massive data centres. Cloud computing scales according to demand to accommodate fluctuating workloads, which has allowed for the ability to scale data generated and consumed by Big Data applications. Cloud virtualization allows for the creation of a virtual platforms of the server operating device and system that can generate multiple machines simultaneously. This allows you for sharing resources and the isolation of hardware in order to improve data access, management and processing of data

Some examples of the sources through which big data is generated include social media, e-commerce data Weather stations, IoT Sensor data etc.

The ability to store huge quantities of data in various forms and then process it at a high speed can result in data that will help educational institutions and businesses in the development of rapid. But there is a major concern about security and privacy concerns when moving to cloud computing which is one of the major reasons for why companies as well as educational institutions are hesitant to make the switch to the cloud. This paper outlines the features and trends, as well as the challenges and opportunities associated with large data. Furthermore it examines the benefits as well as the potential risks that result from the integration of large data as well as cloud computing.

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