Thinking about the development of traditional data center in the era of big data

Thinking about the development of traditional data centers in the era of big data _ Data Analyst Examination

The core value of big data lies in mining valuable information from massive and complex data, making faster analysis and more accurate prediction through big data technology, discovering new business models and creating new business development opportunities. Therefore, in the era of big data, enterprises urgently need to think about how to apply big data technology to transform and improve the existing data center platform, improve the data processing ability of enterprises, improve the level of data analysis, and integrate big data into the overall data scheme of enterprises.

1. Deploying a big data distributed processing framework Distributed processing framework is the basic feature of data center architecture in the era of big data, including distributed storage and distributed computing. Distributed storage adopts an extensible system architecture and uses multiple storage servers to share the storage load. It not only improves the reliability, availability and access efficiency of the system, but also is easy to expand. Distributed computing decomposes a large number of analysis and calculation tasks into several small tasks, then assigns the decomposed tasks to different processing nodes, and finally synthesizes the calculation results to get the final result. Distributed computing has stronger parallel computing ability and scalability, and is suitable for mixed processing of various types of data. Therefore, power grid enterprises need to build a distributed processing framework based on the original data center architecture to improve data storage and processing capabilities.

2. Research and build a big data analysis and processing architecture, sort out the existing technical architecture of power grid enterprise data center, study the key technologies of big data, combine the current mainstream big data processing architecture in the industry, focus on the information infrastructure of data center based on big data platform, explore suitable big data solutions on the basis of protecting the existing information investment of enterprises, and integrate big data into the overall data solution of enterprises. Use big data technology to transform and improve the data center analysis and processing architecture, study the big data information infrastructure that integrates structured data, real-time data, location data and unstructured data, build an enterprise-level big data analysis and mining platform, realize the integration and correlation analysis of different types of data, support the application of big data analysis, and improve the data analysis and mining ability.

3. The core of using big data analysis to create value data is to discover value, and the core of controlling data is analysis. How to harness big data and mine valuable information from massive data is the most important thing. Therefore, enterprises should focus on the hidden value of data, fully tap the core value of data through the application of big data technology analysis, continuously optimize business processes, reduce management costs, assist enterprises in scientific decision-making, and accumulate strength for their continuous innovation and development. ?

The influence of information depends on the ability of data association, and the new insights gained by aggregating multiple large data sets far exceed those gained by a single large data set. For example, in cooperation with crop protection providers and meteorological departments, seed companies have comprehensively utilized many large data sets, including weather data, soil moisture data, soil type data, seed data and other data. Cross-correlation analysis of these data can help growers get higher yield. In power enterprises, data from different data sources such as distribution, electricity consumption, customers and weather will be transformed and integrated, thus generating new commercial value. Through the fusion analysis of power transaction data, climate data, age structure and living habits of customers, we can understand customers' electricity consumption behavior, meet customers' differentiated needs, and open up new value-added business space by tapping deep-seated needs. ?

4. How to do a good job in data-driven business How to do a good job in data-driven business is a key issue that data centers must consider in the era of big data. Traditional data centers are difficult to meet the needs of business departments. In the era of big data, the complexity of data determines that data centers need to respond to the changes and uncertainties of business requirements more quickly. Therefore, the data center must change from the custodian and server of data to the manager and decision maker of data, and from passively responding to the requirements of business departments to providing data services to business departments. Data-driven business refers to the process that data, as a kind of productivity, actively feeds back the information analyzed and mined by data to business decision makers in real time, which affects and feeds back the business of enterprises.

In the era of big data, the whole process analysis, all-round monitoring, simulation and prediction of enterprise business can be carried out, and real-time feedback can be given, so that decisions can be adjusted in time to improve the direction of business development, so that business can instantly perceive from data, and business can evaluate and make decisions with data.

The above is the relevant content of Bian Xiao's thoughts on the development of traditional data centers in the era of big data. For more information, you can pay attention to Global Ivy and share more dry goods.