ETL stands for Extract, Transform, and Load, and refers to the basic pattern for processing and integrating data together from multiple sources. This pattern is used in physical as well as virtual executions, and in batch processing and real-time processing. In general, ETL data flows is a term that can be interchanged with data pipeline, however, data pipelines entail more.
A data pipeline, in comparison to ETL, is the exact arrangement of components that link data sources with data targets.
For example, one pipeline may consist of multiple cloud, on-premise, and edge data sources, which pipe into a data transformation engine (or ETL tool) where specific ETL processes can be specified to modify incoming data, and then load that prepared data into a data warehouse.
Contrastingly, another pipeline may favor an ELT (Extract, Load, and Transform) pattern, which will be configured to ingest data, load that data into a data lake, then transform it at a later point. However, ETL is the more common approach rather than ELT, and so easily associated with data pipelines.