Who's using the big data?

Big data is very hot and it's important to recognize it. What are the areas of segmentation that are being analyzed with real time data? What are the current mainstream technologies for large data analysis? Shanghai Yun Man Technology Co., Ltd., focusing on real-time analysis of large data. CEO Wu Zhuhua, 2006, 2009 years in Zhongguancun Software Park, IBM China Research Institute to do some cloud operating system development work. At the end of 2009, from China IBI China Research Institute left. 2010 returned to Shanghai, wrote a book "Cloud Computing Core technology analysis." 2011, in Shanghai, the formation of cloud people Technology team, launched a product called Yun table. Here is his share of the 2013 Cloud World Congress: The opportunities for large data in various industries are as follows: Financial Securities (High-frequency trading, quantitative trading), telecommunications services (support systems, unified tents, business intelligence), Energy (Power plant power grid monitoring, electricity information collection and analysis), the Internet and the electricity quotient (user behavior analysis, Commodity model analysis, credit analysis, other industries such as Intelligent city, the internet of things. Classic case: Smart city, a city, about a hundred thousand of of the camera in the city, every second will send data to the cloud in the data center, every day with terabytes of data to deal with, and need real-time feedback, the scene needs real-time processing technology. Car networking, we have a customer to do car networking, he probably a city on every computer, have to install terminals, this terminal will send a traffic information sent to the cloud, to send 100 million data into the cloud, and is a number of calculations per minute, real-time judgment of road conditions, to the user the best driving advice. Financial securities, such as financial transactions telephone transactions is a mainstream direction, we have a securities institutions to build a very large cloud platform, there are tens of billions of of data in the background, can provide real-time data analysis, data interface, let them quickly run. Telecom, our side is moving over there is a case. We are in a province where we have all the information on the Internet in a province loaded into our centralization of power, and our centralization of power can give some statistical feedback to them, support some of their business support systems, business skills, and statistical relevance. Energy, mainly used in power grid monitoring, electricity information collection analysis. The electric dealer, the real-time promotion advertisement gives the user, they may do the commodity model analysis, the best product recommendation to the user. For example, the Internet, there is a commodity model, as well as credit analysis. I have a friend is to do credit analysis, within more than 10 seconds of this person's data analysis, to give users a rating, quickly determine whether the user is worth lending to him. Why do you need large data analysis in real time? First, real-time decision-making, quantitative transactions, can be real-time calculation of data, quickly determine whether I buy stocks or not. Second, improve business efficiency. Third, we are free to try new algorithms or new strategies for data. In this way, we can quickly discover new ideas andOpportunity。 IV. provision of operational outputs. What is the challenge of big data? First is to be quick: within 10 seconds, 100 milliseconds for good results. Internet companies, Baidu they want 100 milliseconds to give results. Some financial institutions want to give results in microseconds, require real-time capabilities, and the 1th is fast, real-time analysis. Second, is large, the amount of data for the 1 billion per TB level. Before we thought the data over 10 million was not big. We now run into the largest centralization of power, presumably at a level as close to trillions of data. Third, can do a variety of analytical operations. The simplest is a query, or it can be a logical complex of algorithms and data analysis. What technologies are available? The first is Hadoop. It itself is developed by Google, it is the algorithm in large data, for TB data, there is no problem in large, and operational diversification. Because of his tools on the line there are a lot of algorithms are very good. But it's quick and awkward, he needs less than a minute, he's a lot to do a reduce, it takes a long time. Second, NoSQL (non-relational database). In the big, should be able to support big. HBase can satisfy big features, it can do a big. The hbase is a database and can only support simple queries. HBase difficult to do some logic complex data analysis and mining. For example, Taobao, they may be more rich, they use a lot of hardware and a lot of development costs, a set of hbase data development cluster. For small and medium-sized enterprises, and the traditional enterprise is not too suitable for using no SQL analysis. It requires huge hardware costs and development costs. Does the traditional Oracle database support the analysis of large data? The support algorithm is possible, but it is inherently difficult to compute large data.