What is the relationship of big data to cloud computing and the Internet of Things (IoT)

Cloud computing (cloud computing) is an Internet-based model for the addition, use, and delivery of related services, usually involving the provision of dynamically scalable and often virtualized resources over the Internet. Cloud is a metaphorical term for network and internet. In the past, cloud was often used in diagrams to denote telecommunication networks, but later it was also used to denote the abstraction of the Internet and the underlying infrastructure. Cloud computing in a narrow sense refers to the delivery and use model of IT infrastructure, meaning that the required resources are available in an on-demand, easily scalable manner over the network; in a broader sense, it refers to the delivery and use model of services, meaning that the required services are available in an on-demand, easily scalable manner over the network. Such services can be IT and software, internet related or other services. It implies that computing power can also be circulated as a commodity over the Internet.

Big data, or massive data, refers to information that is so large that it cannot be captured, managed, processed, and organized in a reasonable amount of time through mainstream software tools to help businesses make more positive decisions. The 4V characteristics of Big Data: Volume, Velocity, Variety, and Veracity.

Technically, the relationship between Big Data and Cloud Computing is just as inextricably linked as the positive and negative sides of a coin. Inevitably, big data cannot be processed by a single computer, but must be distributed computing architecture. It is characterized by the mining of massive data, but it must rely on cloud computing distributed processing, distributed database, cloud storage and virtualization technology.

Big data management, distributed for file systems, such as Hadoop, Mapreduce data segmentation and access to the execution; at the same time SQL support, Hive + HADOOP as a representative of the SQL interface support in the big data technology to build the next generation of data warehouses with cloud computing has become a hot topic. From the point of view of system requirements, the architecture of big data presents new challenges to the system:

1, higher integration. A standard chassis maximizes specific tasks.

2. More configurable and faster. Storage, controllers, I/O channels, memory, CPU, network balanced design for data warehouse access optimal design, more than an order of magnitude higher than traditional similar platforms.

3. Lower overall energy consumption. It consumes the least amount of energy for the same computing task.

4. More stable and reliable system. Can eliminate a variety of single point of failure links, unified a component, device quality and standards.

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

6, planning and predictable system expansion, upgrade roadmap.