Database and data warehouse
The database has been widely used in the field of information technology. In every department of our social life, there are almost all kinds of databases holding various data closely related to our lives. As a branch of the database, the concept of the data warehouse is much closer in time than the database. The famous American information engineering expert Dr. William InmON put forward an expression of the concept of data warehouse in the early 1990s, saying: "A data warehouse is usually a subject-oriented, integrated, time-varying, but relatively stable data collection of information itself. It is used to support the management decision-making process. "
The theme here refers to the key aspects that users care about when using the data warehouse to make decisions, such as: revenue, customers, sales channels, etc .; the so-called theme-oriented refers to the information in the data warehouse is organized by theme, not like Business support systems are organized according to business functions.
Integration means that the information in the data warehouse is not simply extracted from various business systems, but undergoes a series of processing, sorting and summary processes. Therefore, the information in the data warehouse is consistent global information about the entire enterprise.
With time, it means that the information in the data warehouse not only reflects the current state of the enterprise, but also records the information from a certain point in the past to the current stage. Through this information, you can make quantitative analysis and prediction of the company's development process and future trends.
Characteristics of data warehouse
The data warehouse is generated when the database already exists in large numbers, in order to further tap data resources and for decision-making needs. It is not a so-called "large database". The purpose of the construction of the data warehouse plan is to serve as the basis for front-end query and analysis. Due to the greater redundancy, the required storage is also larger. In order to better serve front-end applications, data warehouses often have the following characteristics:
1. The efficiency is high enough. The analysis data of the data warehouse is generally divided into days, weeks, months, quarters, years, etc. It can be seen that the data required by the cycle of the day is the most efficient, requiring customers to see yesterday's data analysis within 24 hours or even 12 hours. Because some companies have a large amount of daily data, poorly designed data warehouses often have problems, and data can only be given after a delay of 1-3 days. Obviously, this will not work.
2. Data quality. The various information provided by the data warehouse must be accurate data, but because the data warehouse process is usually divided into multiple steps, including data cleaning, loading, query, display, etc., the complex architecture will have more levels, then because of the data Dirty data from the source or imprecise code can lead to data distortion. Customers who see the wrong information may cause wrong decisions to be analyzed, causing losses, not benefits.
3. Scalability. The reason why some large-scale data warehouse system architectures are complicated is because they take into account the scalability of the next 3-5 years. In this way, it is possible to operate stably without rebuilding the data warehouse system too quickly in the future. It is mainly reflected in the rationality of data modeling. There are more middle layers in the data warehouse scheme, so that the massive data stream has sufficient buffer, so that it will not run because the data volume is much larger.
As can be seen from the above introduction, data warehouse technology can wake up the data accumulated by the enterprise for many years, not only manage these massive amounts of data for the enterprise, but also tap the potential value of the data, thus becoming one of the highlights of the operation and maintenance system of the communication enterprise. Therefore,
Broadly speaking, a data warehouse-based decision support system is composed of three components: data warehouse technology, online analytical processing technology and data mining technology, of which data warehouse technology is the core of the system. Warehouse technology, introduces the main technologies of modern data warehouse and the main steps of data processing, and discusses how to use these technologies in communication operation and maintenance system to bring help to operation and maintenance.
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