ETL is the standard term that is used for Extracting, Transforming, and Loading data. In traditional data warehousing systems, the entire data pipeline consists of multiple ETL steps that follow after each other to bring the data from the source to the target (usually a report on a dashboard). Let's explore this in more detail:
E: Data is extracted from a source. This can be a file, a database, or a direct call to an API or web service. Once loaded with a query, the data is kept in memory, ready to be transformed. For example, a daily export file from a source system that produces client orders is read every day at 01:00.
T: The data that was captured in memory during the extraction phase (or in the loading phase with ELT) is transformed using calculations, aggregations, and/or filters into a target dataset. For example, the customer order data is cleaned, enriched, and narrowed down per region.
L: The data that was transformed is loaded (stored) into a data store...