A data warehouse is built to support management functions whereas data mining is used to extract useful information and patterns from data. Data warehousing is the process of compiling information into a data warehouse.
It is a technology that aggregates structured data from one or more sources so that it can be compared and analyzed rather than transaction processing. A data warehouse is designed to support management decision-making process by providing a platform for data cleaning, data integration and data consolidation. A data warehouse contains subject-oriented, integrated, time-variant and non-volatile data.
Data warehouse consolidates data from many sources while ensuring data quality, consistency and accuracy. Data warehouse improves system performance by separating analytics processing from transnational databases. Data flows into a data warehouse from the various databases. A data warehouse works by organizing data into a schema which describes the layout and type of data. Query tools analyze the data tables using schema.
It is the process of finding patterns and correlations within large data sets to identify relationships between data. Data mining tools allow a business organization to predict customer behavior. Data mining tools are used to build risk models and detect fraud. Data mining is used in market analysis and management, fraud detection, corporate analysis and risk management.
Comparison between data mining and data warehousing:
|Data Warehousing||Data Mining|
|A data warehouse is database system which is designed for analytical analysis instead of transactional work.||Data mining is the process of analyzing data patterns.|
|Data is stored periodically.||Data is analyzed regularly.|
|Data warehousing is the process of extracting and storing data to allow easier reporting.||Data mining is the use of pattern recognition logic to identify patterns|
|Data warehousing is solely carried out by engineers.||Data mining is carried by business users with the help of engineers.|
|Data warehousing is the process of pooling all relevant data together.||Data mining is considered as a process of extracting data from large data sets.|
- Types of Sources of Data in Data Mining
- Data Warehousing
- Data Normalization in Data Mining
- Data Preprocessing in Data Mining
- Data Integration in Data Mining
- Difference between Data Scientist, Data Engineer, Data Analyst
- Data Mining | Set 2
- Data Mining
- KDD Process in Data Mining
- Numerosity Reduction in Data Mining
- Redundancy and Correlation in Data Mining
- Relationship between Data Mining and Machine Learning
- Attribute Subset Selection in Data Mining
- Frequent Item set in Data set (Association Rule Mining)
- Difference between Data Warehouse and Data Mart
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