Big data needs to break through storage and performance bottlenecks.

Big data needs to break through storage and performance bottlenecks.

The core of big data is the ability to analyze large amounts of data.

Under the circumstances that many IT problems need to be solved first, and the value of big data is increasingly prominent, enterprises need to first improve the cost-effectiveness of data centers to meet the changing business needs, increase the application of big data and the construction of related infrastructure, and meet the requirements of high performance, high scalability, high security and high availability of data centers in the big data environment.

The core analysis ability of big data needs strong background support.

The so-called big data, the core depends on the core analysis ability of a large amount of data. However, the influence of big data core analysis ability not only exists in data management strategy, data visualization and analysis ability, but also fundamentally puts forward higher requirements for data center IT infrastructure and even computer room design principles. In order to achieve the abilITy to process a large amount of data quickly and efficiently, it is necessary to optimize the entire IT infrastructure, giving full consideration to five aspects: high energy saving, high stability, high security, high scalability, high redundancy and infrastructure construction. At the same time, it is necessary to solve the problems of large-scale data center deployment, high-speed intranet construction, computer room cooling, strong data backup and so on.

Big data cannot be separated from the construction of benefit-oriented data centers.

An in-depth understanding of the data center economics of big data applications is of great value for improving the actual profit rate of enterprises. Data center economics can provide a framework to help IT managers understand the long-term value impact of total cost of ownership (TCO) of storage. Using data center economics to determine the accurate expenditure of storage decisions and computing resources will help enterprises to systematically and continuously reduce costs and better support enterprises to adopt big data technology.

Big data needs to break through storage and performance bottlenecks.

In addition to the huge data scale, big data applications also mean massive files. Therefore, how to manage the metadata accumulated in the file system layer is a difficult problem. If it is not handled properly, it will affect the scalability and performance of the system, and the traditional NAS system has this bottleneck. Fortunately, the object-based storage architecture does not have this problem. It can manage billions of files in a system without being bothered by metadata management like traditional storage. Object-based storage system also has the ability of wide-area expansion, which can be deployed in many different locations to form a large-scale cross-regional storage infrastructure. In addition, big data applications still have real-time problems, especially those related to online transactions or finance.