Traditional Data Processing

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 ...

In recent years, with the emergence of new forms of information, represented by social networking sites, location-based services, and the rapid development of cloud computing, mobile and IoT technologies, ubiquitous mobile, wireless sensors and other devices are generating data at all times, Hundreds of millions of users of Internet services are always generating data interaction, the big Data era has come. In the present, large data is hot, whether it is business or individuals are talking about or engaged in large data-related topics and business, we create large data is also surrounded by the big data age. Although the market prospect of big data makes people ...

is the traditional data processing method applicable in the large data age? 　　The data processing requirements under large data environment are very rich and data types in large data environment, storage and analysis mining data is large, the demand for data display is high, and the high efficiency and usability are valued. Traditional data processing methods are not traditional data acquisition source single, and the storage, management and analysis of data volume is relatively small, most of the use of relational database and parallel data Warehouse can be processed. To rely on parallel computing to enhance the speed of data processing, transmission ...

For the business staff, especially the data scientists, Informatica's intelligent data platform is not only an intelligent large data preprocessing tool, but also can bring direct value to the enterprise as the business system. Internet companies often emphasize the details and micro-innovation, the product of a certain function to the extreme, so as to firmly attract a large number of users. But enterprise-class vendors are different, preferring to platform their products. The advantage of the platform is that you can integrate as many functions as possible to facilitate the department ...

[Large data 100 points] Presenter: Bai Moderator: Carey Organizer: Zhongguancun Large data Industry Alliance zhongguancun Large Data Industry alliance specially invited white teacher to take the first "Big Data 100" Forum keynote speaker! Bai is a chief engineer of Shanghai Stock Exchange, Ph. D., PhD. She is a ph. D. tutor at the Institute of Information Engineering, Chinese Academy of Sciences. Also serves as the executive director of Chinese Information Society of China, vice chairman of the Securities Sub-Committee of the National Financial Standardization Committee. White teacher research and work in the field across the academic, industrial ...

[Large data 100 points] Presenter: Bai Moderator: Carey Organizer: Zhongguancun Large data Industry Alliance zhongguancun Large Data Industry alliance specially invited white teacher to take the first "Big Data 100" Forum keynote speaker! Bai is a chief engineer of Shanghai Stock Exchange, Ph. D., PhD. She is a ph. D. tutor at the Institute of Information Engineering, Chinese Academy of Sciences. Also serves as the executive director of Chinese Information Society of China, vice chairman of the Securities Sub-Committee of the National Financial Standardization Committee. White teacher research and work in the field across the academic, industrial ...

The data again "big" no useful is equal to zero, to collect "slow data" "Live data" on the internet every moment in the production of data, people's lives everywhere in the various devices, such as computers, mobile phones, smart appliances, sensors and so on, can always leave traces of human behavior, real-time data generation, These increased geometric levels of data deposition on the Web, become large data. These large numbers of data again "big" no useful is equal to zero, to collect "slow data" "Live data ...

"Abstract" when Hadoop enters the enterprise, it must face the problem of how to solve and deal with 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 in the enterprise, how to deal with the original structured data is a difficult problem for enterprises to enter into large data field. Enterprises need large data technologies that can handle both unstructured and structured data. In large data ...

"Abstract" when Hadoop enters the enterprise, it must face the problem of how to solve and deal with 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 in the enterprise, how to deal with the original structured data is a difficult problem for enterprises to enter into large data field. Enterprises need large data technologies that can handle both unstructured and structured data. In large data ...

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 ...