Table of ContentsIntroductionCloud computingFeatures of cloud computingRelationship between cloud computing and big dataLiterature reviewConclusionInterconnection using information technologies in different methods creates large amounts of data. This data requires dispensation and storage. Cloud is an online storage model in which data is stored on many virtual servers. The distribution of Big Data represents a new challenge in IT, particularly in cloud computing. Data processing involves the collection, storage and analysis of data. In this detail, several questions matter: what is the relationship between big data and cloud computing? The answer to these questions will be addressed in this article, where one will study big data and cloud computing, in addition to getting familiar with the relationship between them in terms of security and challenges. In this article, describe the relationship between Big Data and Cloud Computing and review the literature on Big Data for Cloud Computing. Keywords: Big Data, Analytics, BIG dataV's, cloud computing1)Say no to plagiarism. Get a tailor-made essay on “Why Violent Video Games Should Not Be Banned”?Get the original essayIntroductionThe term Big Data has a large number of aspects that make it difficult to method with the usual fact monitoring apparatus or the distribution of an application. The facts come from everywhere: the sensors used to gather, whether in terms of ranking, posting on community media sites, digital photos and videos, obtaining a contract account and receiving the GPS signal of 'a cell phone. We live in a world where data is increasing rapidly due to the second-hand internet, sensors and heavy machinery at a frighteningly rapid rate. According to Gartner, information is growing at a rate of 59% each year. This development can be described by the term of these four Vs. The volume The association or people generate a large quantity of facts is called amount. Today, the amount of data in almost every organization is close to exabyte. According to IBM, more than 2.7 zeta bytes of facts exist in the digital world today. In total, more than 571 new websites are produced by organizations. Speed: The speed at which information is generated, captured and shared is called speed. The project can only capitalize on statistics if they are captured and shared in real time. Variety: Different types of sources provoke facts such as interior, exterior, society and behavior and come together in polar opposite designs such as metaphors, textbooks, videos, audio, etc. Veracity: it refers to the indecision of the facts, that is to say whether the data obtained is accurate or unshakeable. Full-size facts, especially in their formless, semi-structured forms, are chaotic in their setting and it takes a lot of examples and skill to liberate these facts and make them suitable for psychotherapy. The type and nature of data: Big Data comes from multiple sources with sensors and open text such as social media, unstructured data, metadata and other spatial data collected from web logs, GPS, etc. medical devices, etc. Big Data is subject to different practical aspects, it is therefore in a certain number of forms, more: Structured data: these are data systematized in the form of tables or databases to be manipulated. Unstructured data: they characterize the essential data; this is the data that people producedaily in the form of texts, images, videos, messages, log records, clickstreams, etc. Semi-structured data: or multi-structured, we observe a kind of data that is structured but not designed in the form of tables or databases, for example XML or JSON documents. Cloud computing Cloud computing is a rapidly growing expertise that has made its proven in the next cohort of IT industry and companies. Reliable cloud computing capabilities in software, hardware and IaaS delivered over the Internet and remote data centers. Cloud services need to develop a dominant structure to realize large-scale composite computing tasks and cover a series of computing meanings from storage and subtraction to database and application services. The need to store, process and analyze large volumes of data sets has prompted various organizations and individuals to adopt cloud computing. Many technical applications for deep experiments are currently deployed in the cloud and may continue to grow because they can be quickly provisioned and released with minimal management effort or interaction with the service provider. Cloud computing has several favorable aspects to cope with the rapid growth of economies and technological barriers. Cloud computing provides total cost of ownership and allows organizations to focus on their core business without worrying about issues such as organization, elasticity and manageability of resources. Moreover, the combination of the utility model of cloud computing and a rich set of cloud computing, organizations, and storage services provides a very striking situation in which researchers can carry out their experiments. Cloud delivery replicas typically contain PaaS, SaaS, and IaaS.PaaS, such as Google's Apps Engine and Salesforce. com, Force Stage and Microsoft Azure, refer to different resources working on a cloud to provide a computing platform to end users. SaaS, such as Google Docs, Gmail, Salesforce. com and Online Payroll, refer to applications running on remote cloud infrastructure offered by the cloud provider as facilities accessible via the Internet. IaaS, such as Flex scale and Amazon's EC2, refers to hardware equipment running on a cloud provided by service providers and used by end users on demand. The growing appreciation of wireless systems and mobile campaigns is taking complete cloud computing to new heights due to the distribution proficiency, storage volume and inadequate sequence generation of each device. Characteristics of Cloud ComputingCloud computing is a unique dispersed system which means a refined model. NIST has recognized the main characteristics of the cloud, as it summarizes the idea of ​​cloud computing into five characteristics as follows: On-demand self-service: Cloud services send computing resources such as storage and processing as desired and without no human intervention. Widespread network access: Cloud computing resources are available on the network, mobile and smart devices, even sensors can access cloud computing resources. Resource pooling: Cloud platform users share a vast collection of computing resources; Users can control the nature of assets and the geographic location they prefer, but cannot determine the exact physical location of those assets. Rapid elasticity: media resourcesStorage, network, processing units, and applications are constantly accessible and can be scaled up or down in an almost rapid manner, enabling scalability to ensure optimal resource utilization. Measured service: Cloud systems can measure procedures and resource exhaustion as well as monitoring, controlling and reporting in a completely transparent manner. Relationship between cloud computing and big dataCloud computing and big data come together. Big Data provides workers with the ability to use standard computing to process distributed queries across multiple data sets and return the resulting sets in a timely manner. Cloud computing offers the primary engine using Hadoop, a class of dispersed data processing steps. Large data sources from the cloud and web are stored in a fault-tolerant distributed database and processed through a programming model for large data sets with a parallel distributed algorithm in a cluster. The main objective of data visualization, as shown in Figure 2, is to visualize analytical results presented visually through different graphs for decision making. Big Data leverages dispersed storage technology based on cloud computing rather than local storage involved in a processor or electronic trick. Big data evaluation is driven by the rapidly growing cloud applications developed using multiple cloud applications. Technologies must cope with this new environment as managing big data for concurrent processing has become increasingly complicated. MapReduce is a good example of big data processing in a cloud environment; it allows the processing of large amounts of data sets stored in parallel in the cluster. Cluster computing reveals a decent presentation in a dispersed scheme environment, such as CPU control, storage, and network transports. Likewise, Firestone boldly highlighted the ability of cluster computing to provide a conducive situation for data growth. However, Miller argued that lack of data availability is costly because users offload more decisions to analytical methods; incorrect use of methods or weaknesses inherent in virtualized technologies. Therefore, cloud computing not only offers services for the aggregation and distribution of big data, but also obliges as a classic package. For cloud-based big data analysis, some contexts such as Google, Map reduce, Spark, Twister, Hadoop and Hadoop Reduction and ++ are available. These diaries are reserved for the packaging and distribution of figurines. To provide this data it can be some build records like HBase, Big table and Hadoop DB. Literature Review Saeed Ullah, M. Daud Awan & Sikander Hayat khayal et. al. The author identifies some key features that characterize Big Data frameworks and their associated challenges and problems. The authors use various evaluation metrics from different aspects to identify the usage scenarios of these platforms. The author has studied different Big Data resource management frameworks and studied the advantages and disadvantages of each of them. The author carried out the performance evaluation of resource management engines based on seven key factors and each of the frameworks was ranked based on empirical evidence. Blesson Varghese & Rajkumar Buyya et. al. First discuss the evolution of cloud infrastructure and consider the use of infrastructure from multiple providers and thebenefits of decentralizing IT away from data centers. These trends are expected to address the need for a diversity of new IT frameworks that will be available in upcoming cloud organizations. These constructs are believed to affect parts such as people and devices, data-intensive computing, service space, and self-learning structures. The author designs a roadmap of the contests that will need to be addressed to realize the potential of next-generation cloud classification. Qusay Kanaan Kadhim & Robiah Yusof et. This study aims to examine and classify the issues surrounding the implementation of cloud computing, a hot area that needs to be addressed by future research. The author claims that the security problem has become even more complex in the cloud model as new scopes have come into the scope of the problem associated with model data security, user privacy network security and platform and infrastructure issues. This study was designed to highlight the security issues of cloud computing. The results of this study highlight that there are five main issues associated with the implementation of cloud computing: security issues of cloud mobility and government applications, cloud security services and applications, cloud security data, cloud network security issues and cloud security platform and infrastructure issues. These issues provide an open space for future research to address security gaps by providing either a technical approach or an empirical model to mitigate these issues. ConstandinosX, MavromoustakisGeorgios Skourletopoulos, & and. The author presents a review of current research on Big Data, exploring applications, opportunities and challenges, as well as cutting-edge techniques and underlying representations that leverage cloud computing skills, such as Big Data -as-a- service (BDaaS) or analytics as a service (AaaS). The authors suggest that a cost-benefit analysis was also carried out to measure the long-term benefits of adopting Big Data as a Service business models to support data-driven decision-making and communicate results. to non-technical stakeholders. Samir A. El-Seoud, Hosam F. El-Sofany & Mohamed Abdelfattah et. The whole document presents the characteristics, trends and challenges of Big Data. The authors explore the benefits and risks that may arise from the integration between big data and cloud computing. The authors suggest that the main benefit of integrating cloud computing and big data is the availability of data storage and processing power. The cloud has access to a large pool of resources and various forms of infrastructure that can accommodate this integration in the most appropriate way possible; with minimal effort, the environment can be configured and managed to enable an exceptional universe of effort for all requirements of Big Data i.e. data analysis. This in turn offers a lowered complication with great efficiency. The authors claim that current knowledge and development in the field has not dazzled them and gives the cloud a significant advantage as the most practical solution for hosting and processing Big Data environments. Nabeel Zanoon, Abdullah Al Haj & Sufian M Khwaldeh et. al. The authors suggested a term for big data and a model illustrating the relationship between big data and cloud computing. Big data and cloud computing have been studied under severalimportant aspects, and we concluded that the relationship between them is complementary. Big data and cloud computing are an integrated model in the world of distributed network technology. The development of Big Data and its requirements are a factor that motivates cloud service providers for continuous development, because the relationship between them is based on product, storage and distribution as a joint cause. Big Data represents the product and the cloud represents the containers. Big data and cloud computing are evolving rapidly to keep pace with advances in technology and user requirements. SamiyaKhan, Kashish A. Shakil & MansafAlam et. al. The concept of Big Data presents the precise and encrypted analysis methods that can be beneficial for Big Data and provides a catalog of the dominant approaches, contexts and steps that exist for different big data computing replicas. It also assesses the viability of cloud-based big data computing and examines existing challenges and opportunities. Big Data information tells and demands every stage of a hominoid's life. There is no authorized knowledge system that cannot use big data-based keys to improve the creation of conclusions and precise industry demands. However, in order to make this technology commercially viable, research groups need to identify potential “large” data sets and possible analytical applications for the relevant field. That said, the feasibility and commercial viability of such analytical applications must be aligned with business and customer requirements. /Xiaoxia wang & zhanqiang LI ET. Al The authors present the roadmap of big data leveraging cloud computing to make urban traffic and transportation smarter through mining and model visualization. Quickly illustrated data tackles, classifies associations and considers advanced and surprising customs for cooler developed current evidence. Cloud computing cylinders perfectly convert outdated regime services, enable the kingdom to align facility invention with directional approach, and create intelligent executive networks that encourage effective collaboration. O, Izang A. A & Kuyoro S. O et. al. According to the authors, cloud computing, on the other hand, helps tackle the subject of data storage and management. After seeing some of the topics related to big data and cloud computing, specific resolutions have been recommended to improve the two main notions which will result in an extensive snowballing mode in the frequency of adoption of cloud computing by organizations. It is important for administrations to think about how their data will be produced in the future before organizing a haze service in their advertising. The authors suggest that with the future trend of ever-increasing data, which is expected to double every year, research should continue in these two areas to see how the two key concepts can be improved and how problems and challenges can be reduced at a minimum. minimum.Pedro Caldeira Neves, Bardley Schmer & Jorge Bernardino et. al. The cloud environment heavily leverages Big Data solutions by providing a fault-tolerant, scalable and available environment for Big Data systems. Although Big Data systems are influential systems that enable both creativity and punishment to dream on data, there are some concerns that require further study. The additional fight must be operationalized in emerging port procedures and order forms. The authors..