ETL stands for Extract, Transform, Load. This process involves extracting data from various sources, transforming it to fit operational needs and conform to data warehouse schemas, and finally loading it into a target data warehouse or data lake. A common example includes extracting sales data from multiple online platforms, cleaning and aggregating it to remove inconsistencies, and loading it into a central database for analysis and reporting.
This process is fundamental to business intelligence and analytics. By centralizing and standardizing data from disparate sources, organizations gain a unified view of their operations, enabling better decision-making. The historical context stems from the increasing complexity and volume of data generated by businesses, necessitating a structured approach to data integration and management. Effective ETL processes are vital for data quality, ensuring accurate and reliable insights.