Data Warehousing:
A data warehouse is a large-scale, centralized repository used for storing historical data.
- It consolidates data from different sources, cleans it, and structures it for analytical purposes.
- The data in a data warehouse is typically read-only and used for business intelligence, reporting, and decision-making.
- ETL Process: The process of Extracting, Transforming, and Loading data from various operational databases into the warehouse.
- OLAP (Online Analytical Processing): Data warehouses support OLAP systems that enable fast querying and multidimensional analysis of large volumes of data.
- Example: A retail company might have a data warehouse that stores data from various regions, stores, and time periods, which can be analyzed to track sales trends.
Data Mining:
Data mining refers to the process of discovering patterns, correlations, and useful information from large datasets.
- It involves using algorithms and statistical methods to analyze and model data, revealing insights that are not immediately obvious.
- Classification: Categorizing data into predefined classes or groups (e.g., determining if a customer will buy a product based on past behavior).
- Clustering: Grouping data points that are similar in nature (e.g., segmenting customers into groups based on purchasing habits).
- Association: Finding relationships between variables (e.g., products often bought together).
- Regression: Predicting continuous values (e.g., forecasting sales).
- Example: A bank might use data mining techniques to predict which customers are likely to default on loans based on their transaction and credit history.
