Data Warehousing:
A data warehouse is a centralized, large-scale repository designed to store historical and consolidated data collected from multiple sources across an organization.
Thank you for reading this post, don't forget to subscribe!- It is primarily used for data analysis, reporting, and business intelligence rather than routine transaction processing.
- The main purpose of a data warehouse is to integrate data from various operational databases, clean and transform it into a unified format, and make it available for decision-making and strategic analysis.
Key Components of Data Warehousing:
1.) ETL Process (Extract, Transform, Load):
- This is a crucial process where data is:
- Extracted from various operational systems (like CRM, ERP)
- Transformed into a consistent format (cleaned, filtered, structured)
- Loaded into the data warehouse for analysis
2.) OLAP (Online Analytical Processing):
- OLAP enables users to perform multidimensional analysis on large volumes of data.
- It allows operations like slicing, dicing, drilling down, and pivoting to gain meaningful insights from various business perspectives (e.g., sales by region, time, or product category).
Data Mining:
Data mining is the process of analyzing large datasets to discover hidden patterns, trends, relationships, and useful information that are not immediately obvious.
- It combines elements of machine learning, statistics, and database systems to uncover insights that support better decision-making.
- Data mining helps organizations predict behaviors, classify information, detect anomalies, and identify associations between variables.
Key Techniques in Data Mining:
- Classification: This technique involves sorting data into predefined categories or classes.
- Clustering: Clustering involves grouping similar data points into clusters based on shared characteristics.
- Association: This technique finds relationships or correlations between items in a dataset.
- Regression: Regression is used to predict continuous numeric values based on input variables.