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Difference Between Data Mining and Data Warehousing
In this post, we will understand the difference between data mining and data warehousing.
It is a process used to determine data patterns.
It can be understood as a general method to extract useful data from a set of data.
Data is analysed repeatedly in this process.
It is done by business entrepreneurs and engineers to extract meaningful data.
It uses many techniques that includes pattern recognition to identify patterns in data.
It helps detect unwanted errors that may occur in the system.
It is cost-efficient in comparison to other statistical data processing techniques.
It isn’t completely accurate since nothing is ideal in the real-world.
It 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 done by engineers.
Here, data is stored in a periodic manner.
In this process, data is extracted and stored in a location for ease of reporting.
It 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.
Data loss is possible if the data required for analysis is not integrated to the data warehouse.
It stores large amounts of historical data that helps the user in analysing the trends and seasonality to make further predictions.
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