Fast Data Processing

MapReduce: Simple data processing on large cluster

To understand the concept of large data, first from the "Big", "big" refers to the scale of data, large data generally refers to the size of the 10TB (1TB=1024GB) data volume above. Large data is different from the massive data in the past, and its basic characteristics can be summed up with 4 V (Vol-ume, produced, and #118alue和Veloc-ity), that is, large volume, diversity, low value density and fast speed. Large data features first, the volume of data is huge. Jump from TB level to PB level. Second, the data types are numerous, as mentioned above ...

In "2013 Zhongguancun Big Data Day" Big Data Wisdom City Forum, cloud Human Science and Technology CEO Wu Zhuhua brings to the theme "about intelligent city thinking-real-time large data processing opportunities and challenges" speech. He believes that the opportunities for large data in various industries are as follows: Financial securities (high-frequency transactions, quantitative transactions), telecommunications services (support systems, unified tents, business intelligence), Energy (Power plant power grid Monitoring, information collection and analysis of electricity), Internet and electricity business (user behavior analysis, commodity model analysis, credit analysis), other industries such as Intelligent city, Internet of things. Wu Zhuhua ...

If you talk to people about big data, you'll soon be turning to the yellow elephant--hadoop (it's marked by a yellow elephant). 　　The open source software platform is launched by the Apache Foundation, and its value lies in its ability to handle very large data in a simple and efficient way. But what is Hadoop? To put it simply, Hadoop is a software framework that enables distributed processing of large amounts of data. First, it saves a large number of datasets in a distributed server cluster, after which it will be set in each server ...

Large data areas of processing, my own contact time is not long, formal projects are still in development, by the large data processing attraction, so there is the idea of writing articles. Large data is presented in the form of database technologies such as Hadoop and "NO SQL", Mongo and Cassandra. Real-time analysis of data is now likely to be easier. Now the transformation of the cluster will be more and more reliable, can be completed within 20 minutes. Because we support it with a table? But these are just some of the newer, untapped advantages and ...

With the upsurge of large data, there are flood-like information in almost every field, and it is far from satisfying to do data processing in the face of thousands of users ' browsing records and recording behavior data. But if only some of the operational software to analyze, but not how to use logical data analysis, it is also a simple data processing. Rather than being able to go deep into the core of the planning strategy. Of course, basic skills is the most important link, want to become data scientists, for these procedures you should have some understanding: ...

Large data processing technology is changing the current operating mode of the computer. We've got a lot of revenue from that because it's the big data processing technology that brings us search engine Google. 　　But the story is just beginning, and for several reasons, we say that large data processing technology is changing the world: * It can handle almost every type of data, whether it's microblogging, articles, emails, documents, audio, video, or other forms of data. 　　* It works very fast: practically in real time. * It's universal: because it's ...

With the upsurge of large data, there are flood-like information in almost every field, and it is far from satisfying to do data processing in the face of thousands of users ' browsing records and recording behavior data. But if only some of the operational software to analyze, but not how to use logical data analysis, it is also a simple data processing. Rather than being able to go deep into the core of the planning strategy. Of course, basic skills is the most important link, want to become data scientists, for these procedures you should have some understanding: ...

When Hadoop enters the enterprise, it must face the problem of how to address and respond to the traditional and mature it information architecture. 　　In the industry, how to deal with the original structured data is a difficult problem for enterprises to enter large data field. When Hadoop enters the enterprise, it must face the problem of how to address and respond to the traditional and mature it information architecture. In the past, MapReduce was mainly used to solve unstructured data such as log file analysis, Internet click Stream, Internet index, machine learning, financial analysis, scientific simulation, image storage and matrix calculation. But ...

With the current network language, I am a liberal arts man. Mo Yan recently said in his acceptance of the Nobel Prize that literature is not science and literature is useless. I would like to explain that literature is not equal to Uvenko, liberal arts are more extensive, can be further divided into the humanities and social sciences. Social science research has always been dealing with data, of course, used to be small data, the number of low, slow, time-consuming, but good quality, but also provincial resources, in line with the current green concept. On the basis of my experience in studying small data for many years, I am talking about some of the views on large data, which is also a consensus of the social sciences. Readers read ...