Storm Data Processing

First of all: Hadoop is disk-level computing, when computing, data on disk, need to read and write disk; http://www.aliyun.com/zixun/aggregation/13431.html ">storm is a memory-level calculation, Data imports memory directly over the network. Read/write memory is faster n order of magnitude than read-write disk. According to the Harvard CS61 Courseware, disk access latency is about 75,000 times times the latency of memory access. 　　So storm faster. ...

Simple and clear, http://www.aliyun.com/zixun/aggregation/13431.html ">storm makes large data analysis easier and enjoyable. In today's world, the day-to-day operations of a company often generate TB-level data. Data sources include any type of data that Internet devices can capture, web sites, social media, transactional business data, and data created in other business environments. Given the amount of data generated, real-time processing has become a major challenge for many organizations. ...

Real-time data processing application of cloud computing based on storm in automatic clearing system Hu Yuzhou Van Ku Kendao Miuli The large data problem brought by the increasing passenger traffic in the rail transit industry, this paper puts forward the method of using Strom cloud computing technology to solve this problem, and proves that the cloud computing technology is fully applicable to the track automatic traffic ticket (AFC) clearing system (ACC), the application of Storm cloud computing in processing the real-time data service of the clearing system is discussed. The practice shows that the cloud computing based on storm is fully applicable to rail transit ...

Hadoop (the undisputed king of the Big Data analysis field) concentrates on batch processing. This model is sufficient for many scenarios, such as indexing a Web page, but there are other usage models that require real-time information from highly dynamic sources. To solve this problem, we have to rely on the http://www.aliyun.com/zixun/aggregation/13431.html ">storm" that Nathan Marz launched (now called in Twitter.

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 ...

Ufida UAP Data platform has the ability of large data processing and analysis, it mainly relies on unstructured data processing platform Udh (UAP distribute for Hadoop) to complete. 　　UDH includes Distributed file system, storage database, distributed analysis and computing framework for Distributed batch processing, real-time analysis query, stream processing and distributed batch processing based on memory, and distributed data mining. In today's big data, companies can not blindly follow, but should understand why big data is so hot, why pay attention to it. Its ...

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 ...

Depending on the use scenario, large data processing is gradually evolving to two extremes-batch processing and streaming. The streaming processing pays more attention to the real-time analysis of the data, and represents the storm and S4 of the tools. 　　and batch processing is more focused on the long-term data mining, the typical tool is derived from the three major Google paper Hadoop. With the "bursting" of data, companies are racking their brains over large data processing, with the aim of being faster and more accurate. However, the recent new Open-source tool Summingbird has broken the rhythm of ...

The drawbacks of "editor's note" Hadoop are also as stark as its virtues--large latency, slow response, and complex operation. is widely criticized, but there is demand for the creation, in Hadoop basically laid a large data hegemony, many of the open source project is to make up for the real-time nature of Hadoop as the goal is created, Storm is at this time turned out, Storm is a free open source, distributed, A highly fault-tolerant real-time computing system. The storm makes continuous flow calculation easy, making up for the real-time ...

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: ...