This is the input, processing and output model explained below; The diagram illustrates the relationship between cloud computing and big data in detail.
|BASIC||On-demand services are provided through the use of integrated computer resources and systems.||Extensive set of structured, unstructured and complex data that prohibit the traditional processing technique to work on them.|
|Purpose||Allow data to be stored and processed on the remote server and accessed from anywhere.||Organization of the large volume of data and information to extract valuable hidden knowledge.|
|Working||Distributed computing is used to analyze data and produce more useful data.||Internet is used to provide cloud-based services.|
|Advantages||Low maintenance cost, centralized platform, provision for backup and recovery.||Cost effective, scalable, robust parallelism.|
|Challenges||Availability, transformation, security, load model.||Variety of data, data storage, data integration, data processing and resource management.|
Definition of Cloud Computing
Cloud computing It provides an integrated platform of services to store and retrieve any amount of data, at any time, from anywhere and on demand through high-speed Internet. The cloud is a large set of land servers scattered on the Internet to store, manage and process data. Cloud computing is developed for developers to easily implement web-scale computing. The evolution of the Internet has created the cloud computing model, since the Internet is the basis of cloud computing. For cloud computing to work efficiently, we need the high-speed Internet connection. It offers a flexible environment, where capacity and capabilities can be dynamically added and used according to the pay-per-use strategy.
Cloud computing has some essential properties that are resource pooling, self-service on demand, broad network access, measured service and fast elasticity. There are four types of cloud: public, private, hybrid and community.
There are basically three models of cloud computing: Platform as a service (Paas), Infrastructure as a service (Iaas), Software as a service (Saas), which uses hardware and software services.
- Infrastructure as a service : This service is used to deliver the infrastructure, which includes storage processing capacity and virtual machines. It implements the virtualization of resources based on the service level agreement (SLA).
- Platform as a service : is above the IaaS layer, which provides a programming and runtime environment for users to deploy applications in the cloud.
- Software as a service : Deliver applications to the client that run directly on the cloud provider.
Big Data Definition
The data becomes big data with the increase in volume, variety, speed, beyond the capabilities of IT systems, which in turn creates difficulties to store, analyze and process data. Some organizations have developed the equipment and experience to deal with this kind of massive amount of structured data, but the volumes that increase exponentially and the rapid flow of data are no longer capable of extract them and generate actionable intelligence quickly. This bulky data cannot be stored in normal devices and dispersed in the distributed environment. Large data computing is an initial concept of The science from data which focuses on the multidimensional information mining for scientific discovery and business analysis in large-scale infrastructure.
The fundamental dimensions of the massive data are the volume, speed, variety and veracity that were also mentioned above, then two other dimensions are developed that are variability and value.
- Volume : means the increasing size of the data that is already problematic to process and store.
- Speed : is the instance in which data is captured and the data flow rate.
- Variety : The data is not always presented in a single form, there are several forms of data, for example, text, audio, image and video.
- Veracity : refers to the reliability of the data.
- Variability : describes the reliability, complexity and inconsistencies produced in big data.
- Value : The original form of the content may not be very useful and productive, so the data is analyzed and high value data is discovered.
Key differences between Cloud Computing and Big Data
- Cloud computing is the computer service that is delivered on demand through the use of computer resources scattered on the Internet. On the other hand, big data is a massive set of computer data, which includes structured, unstructured, semi-structured data that cannot be processed using traditional algorithms and techniques.
- Cloud computing provides users with a platform to take advantage of services such as Saas, Paas and Iaas, upon request and also charges for the service according to its use. In contrast, the main objective of Big Data is to extract the knowledge and hidden patterns of a huge collection of data.
- High speed Internet connection is an essential requirement for cloud computing. On the contrary, Big Data uses distributed computing to analyze and extract data.
Relationship between cloud computing and big data.
The diagram shown below illustrates the relationship and operation of cloud computing with big data. In this model, the primary input, processing and output computing model is used as a reference in which large data is inserted into the system using input devices such as mouse, keyboard, cell phones and other smart devices. The second stage of processing includes the tools and techniques used by the cloud to provide the services. Finally, the result of the processing is delivered to the users.
Cloud computing technology provides an adequate and compatible framework for big data through ease of use, access to resources, low cost in the use of resources in supply and demand, and also minimizes the use of solid equipment used in handling big data. Both the cloud and big data emphasize increasing the value of a company while reducing the cost of investment.