Data for Wang Hadoop with large data processing

With the advent of the cloud era, large data (Bigdata) has attracted more and more attention. Bigdata, a team of analysts at Yuntai, argues that big data is often used to describe the vast amount of unstructured and semi-structured data that a company creates, which spends too much time and money downloading into relational databases for analysis. Large data analysis is often associated with cloud computing, because real-time large dataset analysis requires a framework like MapReduce to allocate work to dozens of, hundreds of, or even thousands of of computers. In short, the ability to quickly get valuable information from a variety of types of data is a big data technology. It is important to understand this and it is this that has the potential to move to many businesses.
　　
The big data is called "big" because it has four levels: first, the volume of data is huge. Jump from TB level to PB level; second, a wide variety of data types. The aforementioned blog, video, pictures, geographic information and so on. Third, the value density is low. In video, for example, a continuous uninterrupted monitoring process may be useful for only two seconds. Four, the processing speed is fast. 1 Second law. This last point is fundamentally different from the traditional data mining technology.
　　
In large data processing, we have Hadoop,hadoop is a software framework capable of distributed processing of large amounts of information. It is a very good tool for dealing with large data in the Internet industry, and the big data itself goes beyond the internet industry, because the biological gene itself is a big data, and the genetic data produces a lot of academic and commercial value. Use Hadoop to tap large data, exploit larger data to develop greater value, and explore innovative spaces.
　　
While big data is still at an early stage in the country, commercial value has emerged. First, the companies that hold the data are standing on the gold mine, which can produce good results based on data transactions, and secondly, there are many business models based on data mining, different positioning angles, or focus on data analysis. For example, to help enterprises do internal data mining, or focus on optimization, to help enterprises more accurate to find users, reduce marketing costs, improve sales rate, increase profits.