Creating A Data Warehouse

I've found that a lot of big data providers are always trying to prove the superiority of their technology by debasing the Data Warehouse, and I have always hated this way of marketing. They always say that the Data Warehouse system is too large, expensive and inflexible, and that their technology is fast, flexible and inexpensive. In the end they will be smug and say, "Come buy our products, and we'll get you out of the Data warehouse." 　　"They are always implying that you are a technology, or that the solution itself is out of the question. I admit that there are many problems with the data warehouse itself. It's not easy to design a data warehouse, but to be real ...

Big data is now a very hot topic, SQL on Hadoop is the current large data technology development in an important direction, how to quickly understand the mastery of this technology, CSDN specially invited Liang to do this lecture for us. Using Sql-on-hadoop to build Internet Data Warehouse and business intelligence system, through analyzing the current situation of business demand and sql-on-hadoop, this paper expounds the technical points of SQL on Hadoop in detail, shares the experience of the first line, and helps the technicians to master the relevant technology quickly ...

The purpose of data mining is to find more quality users from the data. Next, we continue to explore the model of the guidance data mining method. What is a guided data mining method model and how data mining builds the model. In building a guided data mining model, the first step is to understand and define the target variables that the model attempts to estimate. A typical case, two-dollar response model, such as selecting a customer model for direct mailing and e-mail marketing campaigns. The build of the model selects historical customer data that responds to similar activities in the past. The purpose of guiding data mining is to find more similar ...

The purpose of data mining is to find more quality users from the data. Next, we continue to explore the model of the guidance data mining method. What is a guided data mining method model and how data mining builds the model. In building a guided data mining model, the first step is to understand and define the target variables that the model attempts to estimate. A typical case, two-dollar response model, such as selecting a customer model for direct mailing and e-mail marketing campaigns. The build of the model selects historical customer data that responds to similar activities in the past. The purpose of guiding data mining is to find more classes ...

"IT168 Information" March 12, 2013, IBM held 2013 large data press conferences in Beijing, fully demonstrating its unparalleled industry advantages in large data areas: from the industry's unique large data platform to the world's top large data analysis technology, from large data strategies to large data landing solutions, From a large data strategy for the enterprise consulting to IBM in the global and Chinese markets in different industries, large data practices, ...

Integration strategy Data integration refers to the combination of data from different systems for business users to study different industry behavior and customer behavior data processing methods. In the early days of data integration, data was limited to trading systems and their applications.   The development of business decision is guided by the decision platform, and the limited dataset provides the foundation of creating the decision platform. Data capacity and data types have grown dramatically over the past 30 years, with data warehousing technology from scratch, and infrastructure and technology to meet analysis and data storage requirements. All this has revolutionized the prospects for data integration. ...

Zhang Fubo: The following part of the forum is mainly four guests, talk about cloud practice. Beijing First Letter Group is the Beijing government's integration company, mainly responsible for the capital window of the construction, they are also in the domestic, in the government industry earlier in a company, as the first letter Group Technical Support Center General Manager Zhang Ninglai for us to do the report. Zhang: Good afternoon, we have just introduced, I am from Beijing First Letter Development Co., Ltd., I bring today is the result of our practice in cloud computing technology these years. Today is mainly divided into three parts, we mainly do is the field of e-government applications, we are mainly ...

Storing them is a good choice when you need to work with a lot of data. An incredible discovery or future prediction will not come from unused data. Big data is a complex monster. Writing complex MapReduce programs in the Java programming language takes a lot of time, good resources and expertise, which is what most businesses don't have. 　　This is why building a database with tools such as Hive on Hadoop can be a powerful solution. Peter J Jamack is a ...

[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 ...

Integration strategy Data integration refers to the combination of data from different systems for business users to study different industry behavior and customer behavior data processing methods. In the early days of data integration, data was limited to trading systems and their applications.   The development of business decision is guided by the decision platform, and the limited dataset provides the foundation of creating the decision platform. Data capacity and data types have grown dramatically over the past 30 years, with data warehousing technology from scratch, and infrastructure and technology to meet analysis and data storage requirements. All this has revolutionized the prospects for data integration. ...