Tools Used In Big Data Scenarios

Machine learning is a science of artificial intelligence that can be studied by computer algorithms that are automatically improved by experience. Machine learning is a multidisciplinary field that involves computers, informatics, mathematics, statistics, neuroscience, and more.

Gartner presented the BI concept in 1989. Gartner further upgraded the BI concept to advanced analysis (Advanced Analytics) in 2008. 2011, McKinsey explained the concept of large data. The names are different, but the problems they are trying to solve have never changed. However, now large data analysis technology can deal with more than 20 years ago, a larger, more diverse, real-time (Volume, produced, Velocity) data, that is, large number ...

Sometimes the best way to learn about a software product is to see how it is used. It can solve what problems and how these solutions apply to large application architectures that can tell you a lot. Because HBase has a lot of open product deployments, we can do just that. 　　This section describes in detail some of the scenarios in which people successfully use HBase. Note: Do not limit yourself to the belief that hbase can only solve these usage scenarios. It is a nascent technology, and innovation based on the use of the scene is driving the development of the system. If you have new ideas, think you can benefit from HBAs ...

"Editor's note" This article is reproduced in the public number "Fu Zhihua", the author has been Tencent social networking business Group data center director and Tencent Data Association president.     Before Tencent, he worked in the market consulting, Analysys International, China Internet Association, and served as vice president of Dcci Internet Data Center. The internet industry in the accumulation of large data and applications to Baidu, Tencent and Alibaba is the most noteworthy. Baidu, Tencent and Alibaba in the application of large data although there is a common place, but due to their respective data sources and ...

It has been almost 2 years since the big data was exposed and the customers outside the Internet were talking about big data. It's time to sort out some of the feelings and share some of the puzzles that I've seen in the domestic big data application. Clouds and large data should be the hottest two topics in the IT fry in recent years. In my opinion, the difference between the two is that the cloud is to make a new bottle, to fill the old wine, the big data is to find the right bottle, brew new wine. The cloud is, in the final analysis, a fundamental architectural revolution. The original use of the physical server, in the cloud into a variety of virtual servers in the form of delivery, thus computing, storage, network resources ...

The Data Platform Business Unit first used not the MaxCompute (original ODPS) currently in use, but Hadoop. The original Hadoop cluster was named Cloud Ladder 1. At that time, Alibaba was also developing its own computing platform, which was the original ODPS, and named it. Cloud Ladder 2.

The time is 1948, the location is Northeast China. Liaohsi into the critical phase. For the commander of the four field army Lin Biao, the most important goal after the Jinzhou is to defeat the Kuomintang new six troops. The method used by Lin Biao is to listen to the "intelligence report" every day, and the officers on duty should read out the situation and seizure of the troops. It's almost uniform data, and it's boring. Until one day, Lin Biao suddenly discovered that in a two-armed encounter in the Hu Shack, the ratio of the spear to the spear was slightly higher than the other battles, and the proportion of the car and the cart was slightly higher than the other battles.

The time is 1948, the location is Northeast China. Liaohsi into the critical phase. For the commander of the four field army Lin Biao, the most important goal after the Jinzhou is to defeat the Kuomintang new six troops. The method used by Lin Biao is to listen to the "intelligence report" every day, and the officers on duty should read out the situation and seizure of the troops. It's almost uniform data, and it's boring. Until one day, Lin Biao suddenly discovered that in a two-armed encounter in the Hu Shack, the ratio of the spear to the spear was slightly higher than the other battles, and the proportion of the car and the cart was slightly higher than the other battles.

The time is 1948, the location is Northeast China. Liaohsi into the critical phase. For the commander of the four field army Lin Biao, the most important goal after the Jinzhou is to defeat the Kuomintang new six troops. The method used by Lin Biao is to listen to the "intelligence report" every day, and the officers on duty should read out the situation and seizure of the troops. It's almost uniform data, and it's boring. Until one day, Lin Biao suddenly discovered that in a two-armed encounter in the Hu Shack, the ratio of the spear to the spear was slightly higher than the other battles, and the proportion of the car and the cart was slightly higher than the other battles.

This time, we share the 13 most commonly used open source tools in the Hadoop ecosystem, including resource scheduling, stream computing, and various business-oriented scenarios. First, we look at resource management.