When it comes to Data Mining and Data Warehousing, the concepts of architectural components, infrastructure, and metadata are crucial. Here’s a clear explanation of each term.

1. Architectural Components of Data Warehousing

The architecture of a data warehouse is divided into several key components to ensure smooth data flow, storage, and analysis.

a) Data Sources:

• The origin of raw data (e.g., databases, files, applications, cloud storage, etc.).
• It includes transactional databases, ERP systems, CRM systems, and external data sources.

b) ETL (Extract, Transform, Load) Process:

• Extract: Collects raw data from different data sources.
• Transform: Cleanses, formats, and converts the raw data to a consistent format.
• Load: Loads the transformed data into the data warehouse.

c) Data Staging Area:

• A temporary storage area where data is cleansed, transformed, and prepared for loading into the warehouse.

d) Data Warehouse Repository:

• The central storage system where structured, historical, and analytical data is stored.
• It allows for multi-dimensional analysis and supports Online Analytical Processing (OLAP).

e) Metadata:

• Descriptive information about data (like structure, source, usage, and purpose) stored in a metadata repository.
• It acts as a "data dictionary" and provides insights into how the data is organized.

f) Query and Reporting Tools: