What is the relationship between big data and cloud computing?

Cloud computing is the growth, usage, and delivery of Internet-based services, typically involving the provision of dynamically scalable and often virtualized resources over the Internet. Cloud is a metaphor for the network, the Internet. In the past, clouds were often used to represent telecommunication networks in diagrams, and later they were also used to represent the abstraction of the Internet and the underlying infrastructure.

Cloud computing in the narrow sense refers to the delivery and use mode of IT infrastructure, which refers to obtaining the required resources in an on-demand and easily scalable manner through the network. Cloud computing in a broad sense refers to the delivery and consumption model of services, which refers to obtaining the required services in an on-demand and easily scalable manner through the network. Such services can be IT and 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 more positive business decisions through current mainstream software tools in a reasonable time. 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 heads and tails of the same coin. Big data cannot necessarily be processed by a single computer, and a distributed computing architecture must be adopted. It is characterized by the mining of massive amounts of data, but it must rely on the distributed processing, distributed database, cloud storage and virtualization technology of cloud computing.

Big data management, distributed file systems, such as Hadoop and MapReduce, data segmentation and access execution; At the same time, SQL support, represented by Hive+Hadoop, and the use of cloud computing to build a next-generation data warehouse on big data technology have become hot topics. From the perspective of system requirements, the architecture of big data poses new challenges to the system:

1. Higher integration. A standard chassis maximizes specific tasks.

2. The configuration is more reasonable and faster. The balanced design of storage, controller, IO channel, memory, CPU, and network is optimized for data warehouse access, which is more than an order of magnitude higher than that of traditional similar platforms.

3. The overall energy consumption is lower. Lowest energy consumption for the same computing task.

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 and device.

5. Low management and maintenance costs. The general management of the data collection is fully integrated.

6. Planable and predictable system expansion and upgrade roadmap.

Big data needs a new processing model to have stronger decision-making, insight and process optimization capabilities to adapt to massive, high-growth and diversified information assets. Big data is also a kind of data collection that is large enough to exceed the capabilities of traditional database software tools in terms of acquisition, storage, management, and analysis, and has four characteristics: massive data scale, rapid data flow, diverse data types, and low value density. The strategic significance of big data technology is not to grasp the huge amount of data information, but to professionalize the processing of this meaningful data.

In other words, if big data is compared to an industry, then the key to the profitability of this industry lies in improving the "processing ability" of data and realizing the "value-added" of data through "processing". From a technical point of view, the relationship between Dalangzen data and cloud computing is as inseparable as the positive and negative sides of a coin. Big data cannot necessarily be processed by a single computer, and must adopt a distributed architecture.

It features distributed data mining of massive amounts of data. But it must rely on the distributed processing, distributed databases and cloud storage, and virtualization technologies of cloud computing.

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