Data Consistency Definition

By clearly defining the relevant concepts of large data, enterprises can plan their own data system correctly, and locate the traditional technology and new technical methods appropriately. With the rapid development of it technology and the emergence of new technologies, the industry has generally confused many basic concepts. This is also the case in today's most popular large data fields. The concepts of structured data and unstructured data are frequently cited, but the parties are often diverging. The confusion of the concept of data has greatly influenced the enterprise to plan the data system clearly and correctly. The author of this article from the actual work ...

By clearly defining the relevant concepts of large data, enterprises can plan their own data system correctly, and locate the traditional technology and new technical methods appropriately. With the rapid development of it technology and the emergence of new technologies, the industry has generally confused many basic concepts. This is also the case in today's most popular large data fields. The concepts of structured data and unstructured data are frequently cited, but the parties are often diverging. The confusion of the concept of data has greatly influenced the enterprise to plan the data system clearly and correctly. The author of this article from the actual ...

The previous article introduced some basic concepts of data quality, data quality control as the basic link of Data Warehouse, is the foundation of guaranteeing the application of upper layer data. Data quality assurance consists mainly of data Profiling, data auditing and data revision (information correcting) three parts, the previous article describes the content of the information Profiling, Profiling the data in the process of obtaining the summary statistics, so the following ...

Recently, I returned to attend some university seminars, government departments of the symposium and the training activities of enterprises, the theme is large data, nowadays, the major domestic newspapers and magazines are also discussing this hot topic, but I found that the Chinese society on the "big data" concept still some inaccurate understanding or even the concept of misunderstanding, In particular, the strategic significance of large data at the national level is underestimated and needs to be deepened. Small data to large data "big data" is a new wave of technology, but also gradually formed a historical phenomenon, its specific refers to the increase in the amount of information storage, the human in practice gradually recognized ...

The storage system is the core infrastructure of the IT environment in the data center, and it is the final carrier of data access. Storage in cloud computing, virtualization, large data and other related technologies have undergone a huge change, block storage, file storage, object storage support for a variety of data types of reading; Centralized storage is no longer the mainstream storage architecture of data center, storage access of massive data, need extensibility, 　　Highly scalable distributed storage architecture. In the new IT development process, data center construction has entered the era of cloud computing, enterprise IT storage environment can not be simple ...

May 2014, the Executive Office of the United States (Executive Office of the President) released the 2014 global "Big Data" white Paper – Big Data: Seize the opportunity, Guardian value (Bigdata:seize opportunities, Preserving Values) (hereinafter referred to as "white Paper"), the United States of large data application and management of the status quo, policy framework and improvement proposals are focused on. The value represented by the white paper ...

Large data is not a patent in the ICT industry. At present, the financial, radio and television and other traditional industries are actively using the power of large data to help enterprises to achieve transformation. In the power industry, large data has been regarded as an important issue at the strategic level of the enterprise: The State Grid has set up three large data centers in Beijing, Shanghai and Shaanxi, of which more than 10,200 sensors have been installed in the large data center of Beijing, which can save a value of about 300,000 yuan per month. So how does the power industry apply big data? In the face of the challenges of the power industry, what are the common features of telecommunications and what are the opportunities for telecommunications? Electricity ...

Large data is not a patent in the ICT industry. At present, the financial, radio and television and other traditional industries are actively using the power of large data to help enterprises to achieve transformation. In the power industry, large data has been regarded as an important issue at the strategic level of the enterprise: The State Grid has set up three large data centers in Beijing, Shanghai and Shaanxi, of which more than 10,200 sensors have been installed in the large data center of Beijing, which can save a value of about 300,000 yuan per month. So how does the power industry apply big data? In the face of the challenges of the power industry, what are the common features of telecommunications and what are the opportunities for telecommunications? Electricity ...

In 2017, the double eleven refreshed the record again. The transaction created a peak of 325,000 pens/second and a peak payment of 256,000 pens/second. Such transactions and payment records will form a real-time order feed data stream, which will be imported into the active service system of the data operation platform.

The purpose of data warehouse is to build an integrated data environment oriented to analysis, and to provide decision support for enterprises (Decision-support). In fact, the data warehouse itself does not "produce" any data, at the same time does not need to "consume" any data, data from the outside, and open to external applications, which is why called "Warehouse", not called "factory" reasons. Therefore, the basic structure of the data warehouse mainly contains the data inflow and outflow process, can be divided into three layers-source data, data Warehouse, data application: From the graph can be seen data warehouse data from ...