Can you explain the difference between ETL, ELT, and another data integration approach? I'm not looking for the basic functional differences, but rather the underlying principles or use cases.

ETL (Extract‑Transform‑Load) and ELT (Extract‑Load‑Transform) differ mainly in where the transformation step occurs. In ETL, data is extracted from source systems, transformed in an intermediate staging area, and then loaded into the target warehouse. This model is useful when the target cannot perform complex transformations or when data quality rules must be applied before load. ELT, on the other hand, loads raw data directly into the target (often a cloud data warehouse like Snowflake or BigQuery) and performs transformations within that system. ELT takes advantage of the target’s compute power, simplifies the pipeline, and reduces data movement. I choose ETL when the target is a legacy database with limited processing capability or when strict data cleansing is required before load. ELT is preferred when the target can handle transformations efficiently, when I want to keep the pipeline simple, and when I need to support ad‑hoc analytics on raw data. The decision also depends on cost, latency, and the skill set of the team.