Adopt flexible and effective methods to plan, design, and implement Basic Data Warehouse solutions based on IBM DB2 Data Warehouse Edition. Part 1 of this series will focus on the customer interaction process and plan data warehouse
Data Warehouse and data mining--a sharp weapon to participate in the competition of digital telecommunication enterprises
The solution of Guangdong Telecom Data Warehouse based on Sybase
Guangdong Institute of Telecommunication Science and technology
1 overview
With the o
Schema | Data Microsoft Data Warehouse architecture
Absrtact: This article briefly introduces the data warehouse using the Microsoft Data Warehouse architecture, discusses the function
Design | data
[For the convenience of their own reading so collect and organize here, Www.DMResearch.net]
21 Principles of Data Warehouse design
--7 steps, 7 taboos and 7 ways of thinking
Seven steps to efficiently implement a data warehouse
The
21 principles of data warehouse design-7 steps, 7 taboos and 7 ideas
Seven steps for efficient data warehouse implementation
The data warehouse has some kinship with our common RDBMS systems, but it is different. If you have n
Seven steps for efficient data warehouse implementation
The data warehouse has some kinship with our common RDBMS systems, but it is different. If you have not implemented a data warehouse, from setting goals to providing design
Data Warehouse--digging in "beer and diapers" 01-5-21 04:19:25
Interlocutor: Host: Cheng Hung--"Computer World" reporter Home: Mengxiao--Renmin University of China School of Information Professor Qi-"Data Warehouse Road" website host it Manufacturers: Yang Shunsheng--NCR Greater China Market and partner g
How data builds a bank data warehouse
Song Yuchang, No. 11th Xinhua East Road, Dengzhou, Henan, China.
As a new technology in data management field, the essence of Data Warehouse technology is to put forward a comprehensive solut
A data warehouse is an environment rather than a product that provides current and historical data for decision-making support. This data is difficult or cannot be obtained in traditional operational databases. Data Warehouse Tech
Original address
I. Definition of metadata
According to the traditional definition, metadata (Metadata) is data about data. In the Data warehouse system, metadata can help data warehouse administrators and developers of
CIF architecture is represented in the diagram
Therefore, a basic infrastructure such as CIF must be able to absorb and support enterprise information from different sources. Not only does it need to invest heavily in unstructured data from file management systems, internal and external Web sites, operating systems, groupware, and e-mail, but it also invests in traditional structured
Perhaps many people understand that the data warehouse is built on the basis of multidimensional data model for OLAP data platform, through the previous article-the basic architecture of the data Warehouse, we have seen that the
Data warehouses are subject-oriented, integrated, non-updatable, and changing over time, which determines that the system design of a data warehouse cannot be designed with the same design method as the traditional OLTP database.The original requirements of the Data Warehouse
Objective:Ready to systematize a set of distributed Data Warehouse Modeling Practice Guide, the first list of the table, is to design a goal for themselves.The first part of the basic articleChapter One concept and definition of data Warehouse1.1 Data Management System1.2 Data
Data Warehouses are subject-oriented, integrated, non-updatable, and constantly changing over time, these features make sure that the system design of the data warehouse cannot be the same as that of the traditional OLTP database.
The original requirements of the data warehouse
Data Warehouse is a comprehensive technology and solution based on data management and application. The successful implementation of data warehouses has a significant impact on the cultivation of a culture of knowledge sharing. Currently, the data
The common dwh architecture is as simple as figures 2 and 3. Generally, for an enterprise, the data lifecycle is 5-7 years, especially for detailed data. The lower the data granularity level, the shorter the lifecycle, the higher the data granularity and the longer the lifecycle. For the flow account
Just a few days ago, a user participated in a job interview for an enterprise. He applied for the DBA of the company and was responsible for data analysis. This company successfully completed the process. Until the person in charge of the company. The owner only gave him an interview question: Let's talk about the differences between the database and the data warehouse
According to the Informix Data Warehouse System implementation methodology, we can divide the data warehouse implementation into the following steps:
1. Business Demand Analysis
Business Requirement analysis is the basis for data warehou
Review
As more and more organizations of data from the GB, TB level to the PB level, marking the entire social informatization level is entering a new era-the big data era. The processing of massive data, analytical ability, increasingly becoming the key factor in the future of the organization in this era, and based on the application of large
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