Big Data storage management technology for attention

At present, the telecom, finance, retail and other industries want to use big data analysis means to help themselves make rational decisions. In particular, the telecommunications and financial industry performance is particularly prominent, market data can not be connected with user consumption data. The first problem they face is the problem of massive data storage. Most companies are trying to build their own data centers to meet large-scale data volumes, or to choose Big data-related tools to deal with, such as Big Data magic mirrors. Chengdu UI Design Training organization However, with the further increase of data, the performance of many data query and analysis decreased sharply, and some data centers even appeared in an unresponsive condition, which brought a great loss to the business.

Corporate CIOs have this concern about how data management strategies can effectively protect data and, when needed, turn data into value at any time. Only by matching the data with the appropriate storage system, the strategy of managing the data can be cost-effective, high-reliability and high-efficiency in dealing with large amounts of data. For enterprises, the first problem facing big data is the cost and time effect problem. Business opportunities are not to be missed, and storage data management, through automated, disk and deduplication, backup and archiving software, allows the enterprise's key data to be divided into different regions, and then according to the specific business needs, the data extraction, operation and analysis, and the formation of the enterprise needs of the target data. Big data faces storage challenges that are solved.

Big data concerns are rising, and big data management technologies are emerging. Among the many technologies, there are 6 kinds of data management technologies, namely, distributed storage and computing, memory database technology, column database technology, cloud Database, NoSQL, Mobile database technology. Among them, distributed storage and computing are of highest concern.

The distributed storage and computing architecture allows large amounts of data to be processed in a reliable, efficient, and scalable manner. Because it works in parallel, data processing is relatively fast and costs are low, and Hadoop and NoSQL are among the areas of distributed storage technology.

In-memory database technology can be used as a separate database, providing instant responsiveness and high throughput for applications, and SAP Hana is a typical representation of this technology.

The characteristic of the column database is that it can better deal with the queries in the massive relational data, the Chengdu Big Data training organization occupies less storage space, which is also one of the ideal architectures to build the Data warehouse.

Cloud databases can be freely extended without the benefit of any deployment environment, providing customers with the virtual capacity they need, and self-service provisioning and self-service usage metering. Big Data training institutions currently Microsoft SQL Server can provide similar services.

NoSQL databases are suitable for scenarios where large amounts of data, extreme query volumes, and schema evolution are used. With the benefits of high scalability, high availability, low cost, predictable resiliency, and architectural flexibility, Oracle launched the Oracle NoSQL database in 2011.

Mobile database technology is adapted to mobile computing products. With the popularization of Intelligent Mobile terminal, people's demand for real-time processing and management of mobile data is increasing, the mobile database has the features of platform mobility, frequent disconnection, diversity of network conditions, asymmetry of network communication, high scalability and low reliability of the system and limitation of power supply capability, etc. It is precisely because these characteristics are valued by the industry.

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Big Data storage management technology for attention