The Relation and Difference between Data Warehouse and Data Mining

We know that data warehouse is a place to store huge data, and data mining is to extract data from massive data. Therefore, there are obvious differences between them in essence, but they are both different and interrelated. Data warehouse and data mining can be regarded as a collection of business intelligence tools. Let's have a good understanding of the definition, connection and difference between data warehouse and data mining!
Data warehouse and data mining
First. Definition
Data warehouse is a kind of conceptual upgrade of database, which can be said to be a new database designed to meet the new needs. This database needs to accommodate more data and a larger data set. Logically speaking, there is no difference between data warehouse and database. It provides strategic collection of all types of data support for decision-making process at all levels of the enterprise. It is mainly used for data mining and data analysis. It is created for the purpose of eliminating message islands and supporting decision-making based on the establishment of data sandbox.
Second. Contact
Data warehouse and data mining are both new decision support technologies. But they have totally different ways of supporting decision-making. Data warehouse stores a large number of decision-making data, which provides different users with random query, comprehensive information or trend analysis information. Data mining is to use a series of algorithms to mine the hidden information and knowledge in data, so that users can use them in decision-making. In a word, data warehouse is to prepare for data mining. Data mining can be built on data warehouse, and the ultimate purpose of both is to improve the information competitiveness of enterprises.
Third. Differences
Data warehouse is a kind of storage technology, its data storage capacity is 100 times of the general database, it contains a large number of historical data, current detailed data and comprehensive data. It can adapt to different users to provide data and information for different decision-making needs. Data mining is developed from artificial intelligence machine learning. It studies all kinds of methods and technologies, and extracts useful information and knowledge from a large number of data.
1. Different functions:
Data warehouse is to support complex analysis and decision-making. Data mining is to find predictive and analytical information in massive data, which is mostly used for prediction.
2. Different developments:
Data warehouse is the first step of data mining. Through the construction of data warehouse, the efficiency and ability of data mining is improved, and the data in data mining is guaranteed to be broad and complete.
3. Different operation:
Data warehouse is generally called OLAP, which analyzes the historical data of some subjects and supports management decision. Data mining is based on the data in data warehouse and multi-dimensional database to find the potential pattern of data to predict, it can deal with the data complex. In most cases, data mining is to let data from data warehouse to data mining database.
The relation and difference between data warehouse and data mining（ www.zcmorefun.com ）Representation mining useful information and knowledge from data warehouse is the biggest purpose of building data warehouse and using data mining. How to mine useful data from data warehouse is the research focus of data mining, the essence and process of which are different. In other words, the data warehouse should be established first, so that data mining can be carried out efficiently, because the data contained in the data warehouse itself is clean (there will be no wrong data mixed in it), complete, and integrated. So the relationship between them can be described as "data mining is a process and technology to find useful information from huge data warehouse"