云计算和大数据哪个更简单一点(大数据与云计算的区别在哪)

Overview of cloud computing and big data
Cloud computing is the addition, use and delivery model of Internet-based related services, usually involving Provide dynamically scalable and often virtualized resources over the Internet. Cloud is a metaphor for network and Internet. In the past, cloud was often used to represent telecommunications networks in diagrams, and later it was also used to represent the abstraction of the Internet and underlying infrastructure. Cloud computing in the narrow sense refers to the delivery and use model of IT infrastructure, which refers to obtaining the required resources through the network in an on-demand and easily scalable manner; in the broad sense cloud computing refers to the delivery and use model of services, which refers to the on-demand and easily scalable manner through the network. way to get the services you need. Such services can be IT, software, Internet-related, or other services. It means that computing power can also be circulated as a commodity through the Internet.
Big data, or massive data, refers to the amount of data involved that is so large that it cannot be captured, managed, processed, and organized into helpful information within a reasonable time through current mainstream software tools. Information for a more positive purpose in corporate business decisions. The 4V characteristics of big data: Volume, Velocity, Variety, and Veracity.
From a technical point of view, the relationship between big data and cloud computing is as inseparable as the two sides of the same coin. Big data cannot be processed by a single computer, and a distributed computing architecture must be used. Its characteristic lies in the mining of massive data, but it must rely on distributed processing, distributed database, cloud storage and virtualization technology of cloud computing.
Big data management, distributed file system, such as Hadoop, Mapreduce data segmentation and access execution; at the same time, SQL support, SQL interface support represented by Hive+HADOOP, using cloud computing to build the next generation of big data technology Data warehousing has become a hot topic. From the perspective of system requirements, the architecture of big data poses new challenges to the system:
1. Higher integration. A standard chassis completes a specific task to the maximum extent possible.
2. The configuration is more reasonable and the speed is faster. The balanced design of storage, controller, I/O channel, memory, CPU, and network, as well as the optimal design for data warehouse access, are more than an order of magnitude higher than traditional similar platforms.
3. The overall energy consumption is lower. For the same computing tasks, the energy consumption is the lowest.
4. The system is more stable and reliable. It can eliminate various single points of failure and unify the quality and standards of a component or device.
5. Low management and maintenance costs. Routine management of data collections is fully integrated.
6. Plannable and foreseeable system expansion and upgrade roadmap.
The relationship between cloud computing and big data
To put it simply: cloud computing is the virtualization of hardware resources, while big data is the efficient processing of massive data. Although this explanation is not entirely appropriate, it can help people who don't understand the two names quickly understand the difference. Of course, if the explanation is more vivid, cloud computing is equivalent to our computers and operating systems, which virtualize a large number of hardware resources and then allocate them for use.
It can be said that big data is equivalent to a "database" of massive data. Looking at the development of the field of big data, we can also see that the current development of big data has been developing in a direction similar to the traditional database experience. In one sentence, it is , traditional databases provide enough space for the development of big data.
The overall architecture of big data includes three layers: data storage, data processing and data analysis. The data must first be stored through the storage layer, and then the corresponding data model and data analysis index system can be established according to the data needs and goals to analyze the data to generate value.
The intermediate timeliness is achieved through the powerful parallel computing and distributed computing capabilities provided by the intermediate data processing layer. The three cooperate with each other, which allows big data to generate ultimate value.
Regardless of the current development of cloud computing, the future trend is: cloud computing, as the bottom layer of computing resources, supports upper-layer big data processing, and the development trend of big data is real-time interactive query efficiency and analysis capabilities. , to borrow the words from a Google technical paper: "You can operate PB-level data in Miaji by moving the mouse." It is really exciting.

Cloud computing is a type of distributed computing, which refers to the decomposition of huge data computing processing programs into Countless small programs are then processed and analyzed through a system composed of multiple servers to obtain results and return them to the user.

What does cloud computing mean?

Cloud computing is an online service that can provide services anytime and anywhere as long as there is a network. It is supplied on demand and charged according to volume. Cloud computing is a model that provides services through the network through computing virtualization. How to use virtualization technology on the cloud server to centrally manage and dynamically schedule IT hardware and software resources does not need to be considered as an ordinary user. Just get the computing power, storage space and information services you need.

Cloud computing is actually like running water at home. In order to drink clean tap water, is it necessary to build a water plant in our home? Obviously not needed. Just turn on the tap to get the water you want to drink. Cloud computing provides everyone with a model, which is actually like tap water. Whatever you want to get in the future, you don’t need a big hard drive or a computer with very strong processing power. You can get it anytime and anywhere as long as you need it. This new type of computing has brought everyone a new way of obtaining information or an information usage model in the ubiquitous network environment, which is the cloud computing model.

The difference between cloud computing and big data

Big data is more about solving business needs and problems, while cloud computing or cloud storage is more about a solution to big data problems. During the application process, the business poses issues such as high concurrency, high performance, effective management of massive data, rapid access, and resource measurement to the system.

Big data is more of a technology for processing massive data based on relational database theory and distributed computing theory, covering data collection, data integration, data storage, data analysis and mining, In all aspects such as data visualization and display, the essence is to build a data-centered storage and management system and platform. Cloud computing is more based on infrastructure-related virtualization technology, software development platform architecture and application development technology. Its essence is to build business models and platforms covering infrastructure, software platforms, application development and other levels.