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Difference Between Data Mining and Data Warehousing
Data mining is a process of extracting useful information and data patterns from data, whereas a data warehouse is a database management system developed to support the management functions. Read this article to learn more about Data Mining and Data Warehousing and how they are different from each other.
What is Data Mining?
Data Mining is a process used to determine data patterns and extract useful information from data. It can be understood as a general method to extract useful data from a set of data. In the data mining process, data is analyzed repeatedly to find patterns.
Data mining is generally done by business entrepreneurs and engineers to extract meaningful data. It uses many techniques that includes pattern recognition to identify patterns in data. It also helps to detect unwanted errors that may occur in the system.
The major advantage of data mining is that it is cost-efficient in comparison to other statistical data processing techniques. However, it is not completely accurate since nothing is ideal in the real-world.
What is Data Warehousing?
Data Warehousing is a database system that has been designed to perform analytics. It combines all the relevant data into a single module.
The process of data warehousing is generally done by engineers. In a data warehouse, data is stored in a periodic manner. In this process, data is extracted and stored in a location for ease of reporting. Also, a data warehouse is updated at regular intervals of time. This is the reason why it is used in major companies, in order to stay up-to-date. It helps simplify every type of data for business. However, data loss is possible if the data required for analysis is not integrated with the data warehouse.
A data warehouse stores huge volumes of historical data that helps the user in analyzing the trends and seasonality to make further predictions.
Now, let us discuss the important differences between data mining and data warehousing in detail.
Difference between Data Mining and Data Warehousing
The following table highlights all the major differences between data mining and data warehousing −
Factor |
Data Mining |
Data Warehousing |
|---|---|---|
Definition |
Data mining is a processing of finding hidden information and patterns in different data sets. |
Data warehousing is a large relational database management system designed to analyze data. |
Function |
Data mining extracts useful information and insights from a large amount of data. |
Data warehousing combines a large about of related data. |
Implementation |
Data mining is implemented after data warehousing to withdraw useful insights. |
Data warehousing is implemented before data mining in which the data is compiled and stored in a common database. |
Advantages |
The major advantages of data mining include helpful in prediction of trends, financial analysis, marketing analysis, and recognition of fraudulent. |
The advantages of data warehousing include easy data access, consistent data storage, and enhanced response time. |
Performer |
Data mining is performed by business entrepreneurs and engineers. |
Data warehousing is performed by engineers. |
Applications |
Data mining is used to identify the relationships and patterns in data. |
Data warehousing is used to consistently organize very large amount of data. |
Conclusion
The most significant difference between the two is that data mining is carried out to identify relationships, patterns, and extracting useful information from different data sets; while data warehousing is carried out to combine extremely large sets of related data.
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