Data mining and data warehouse both come under the area of data analytics. Data mining is extracting valuable data from the stored data in the database. We use the extracted data for data analysis and pattern discovery.
As shown below, the data mining process includes data collection, understanding of the data, data processing, modeling, and evaluation.
A Data warehouse is a process in which data is collected from different resources and integrated into one comprehensive database. We use it to perform queries over the collected data.
In the data warehouse, data is extracted from the sources and is transformed and loaded in the data warehouse, which is then accessed by different users.
Though data mining and data warehouse belong to the same branch, they have certain differences that draw lines between them.
Following are the main differences between data mining and data warehouse:
Data mining | Data warehouse |
Data mining extracts valuable data from stored data in the database. | A data warehouse collects all valuable data for business insights. |
It uses pattern recognition logic to identify patterns. | It includes data extraction and storage for reporting and analysis. |
Data is regularly analyzed. | Data is periodically stored and depends on the stage in which data resides in the data warehouse. |
Data mining extracts data from the data stored in the warehouse. | It extracts data from different resources to make meaningful information. |
Data mining does not ensure accuracy and can cause data mishandling if not appropriately treated. | During data collection, data might not be adequately filtered and can lead to the storage of irrelevant data. |
It predicts the expected results, deals with large data sets, and creates actionable information. | It provides data related to a particular subject, not the ongoing operations, for a defined period. Moreover, once the data is entered, it is not changed(it is non-volatile). |
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