The ETL (Extract, Transform, Load) process is a critical data integration method used in data warehousing to collect, transform, and store data from multiple sources into a central repository. It consists of three main stages:
1. Extract: Data is collected from various heterogeneous sources such as databases, flat files, APIs, or cloud storage.
2. Transform: The extracted data is cleaned, normalized, and formatted to fit the target schema, often involving data enrichment, filtering, and aggregation.
3. Load: The transformed data is loaded into the data warehouse, ready for analysis and reporting.
ETL ensures that data is consistent, high-quality, and ready for querying. It helps businesses integrate data from multiple systems, providing valuable insights for decision-making. The process can be batch-oriented or real-time, depending on the requirements of the organization. Effective ETL is crucial for maintaining an accurate and efficient data warehouse
