How to use big data to perfect data center platform

With the deep application of big data, the value of big data is becoming more and more important, and the core value of big data lies in digging up valuable information from massive and complex data, making faster analysis through big data technology, predicting more accurately, 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 use big data technology to improve the existing data center platform, enhance the data processing ability of enterprises, improve the level of data analysis, and integrate big data into the enterprise's overall data plan. CDA data analysts cover all the skills required by a domestic enterprise recruitment data analyst, including statistical knowledge, software applications (SPSS/SAS/R, etc.), data Mining, database, report writing, project experience, etc. CDA Data Analyst employment prospects can be selected in the communications, medical, banking, securities, insurance, manufacturing, business, market research, research, education and other industries and fields. To perform different data analysis tasks based on three different levels. After the students have finished their studies, they can apply for the data analyst Certificate issued by the Ministry of Industry and Information technology Education and Examination Center.
1. Deploy 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 uses a scalable system architecture that leverages multiple storage servers to share storage load, which not only improves system reliability, availability, and access efficiency, but also scales easily. Distributed computing decomposes a large number of analytic computation tasks into small tasks, then assigns the decomposed tasks to different processing nodes, and finally synthesizes the results to obtain the final result. Distributed computing has stronger parallel computing power and expansibility, and is suitable for the mixed processing of multi-type data, so grid enterprises need to build distributed processing framework and improve data storage and processing ability based on the original data Center architecture.
2. Research and construction of big data analysis and processing architecture
Combing the existing technology structure of grid enterprise data center, researching the key technology of big data, combining with the big data processing architecture of the current industry, focusing on the data center information infrastructure based on big database platform, on the basis of protecting enterprise's existing information investment, exploring the suitable big data solution. Integrate big data into your enterprise's overall data solution. Using big data technology to improve the data center analysis and processing architecture, research the big data information infrastructure that combines structured data, real-time data, location data and unstructured data, build enterprise-class big data analysis and mining platform, realize fusion integration and correlation analysis of different types of data, support big data analysis application, Improve data analysis and mining capabilities.
3. Create value with Big data analytics
The core of the data is the discovery of value, and the core of harnessing the data is analysis. How to harness big data, how to mining valuable information in the massive data is the most important, so enterprises should focus on the hidden value of data, through the application of Big Data technology analysis, to fully tap the core value of data, optimize business processes, reduce management costs, assist enterprises to make scientific decisions, For the continuous innovation and development of enterprises to accumulate strength.
The impact of information depends on the ability to correlate data, and the new insight gained from aggregating multiple large datasets goes well beyond the insights gained from a single big data set. For example, the seed company, in collaboration with crop protection providers and meteorological authorities, has used multiple large datasets, including weather data, soil moisture data, soil type data, seed data and other data, to cross-correlate these data to help growers reap higher yields. In the power enterprise, the data from the distribution, electricity, customer, weather and other data sources can be transformed and integrated, which will generate new business value. The analysis of power transaction data, climatic data and customer's family age structure, living habits and so on, to understand customer's electricity behavior, to meet customer's differentiated demand, and to open up new value-added business space by exploring deep demand.
4. How to make data-driven business
How to get data to drive business is a key issue that the data center must think about in the big data age. Traditional data centers are struggling to meet the needs of the business, and in the big data age, the complexity of data dictates that data centers need to respond more quickly to changes and uncertainties in business needs, so data centers must be transformed into data managers and decision makers by the custodians and service providers of mountain data. The business unit provides data services from a reactive response to the requirements of the business unit.
Data-driven business refers to the process that data is used as a kind of productive force to feed the information of data analysis to the business decision-makers in real-time and to influence and nurture the business.
In the era of big data, the enterprise business can be full-process analysis, all-round monitoring, simulation and prediction, real-time feedback, and timely adjustment of decision-making to improve business development direction, so that the business can be perceived immediately from the data, the business can use data evaluation and mountain data decision, in order to really play the practical value of big data.

How to use big data to perfect data center platform