What is the difference between ETL and ELT?

WHAT THIS TESTS This evaluates whether you understand not just the acronyms but the architectural shift behind them: where transformation compute runs and why cloud warehouses changed the default.

A GOOD ANSWER COVERS ETL extracts data from sources, transforms it on a dedicated processing engine or staging server into the target's clean schema, then loads the finished result into the warehouse. This was standard when warehouse storage and compute were expensive and tightly coupled, so you cleaned data before paying to store it. ELT extracts data and loads it raw into the warehouse first, then transforms it in place using the warehouse's own scalable compute, often expressed as SQL. ELT became practical with cloud warehouses that separate storage and compute and scale elastically. ELT keeps the raw data available for reprocessing under new requirements, supports schema-on-read for semi-structured data, and shifts transformation work to a powerful engine you already pay for.

COMMON WRONG ANSWERS Saying the only difference is the order of letters, missing that the location of transformation compute and the availability of raw data are the real distinctions. Claiming ELT is always better; ETL still suits sensitive data needing masking before landing, or strict pre-load validation. Ignoring data governance, since loading raw can mean loading sensitive fields untransformed.

LIKELY FOLLOW-UPS How does separating storage and compute enable ELT? When is ETL preferable for compliance or PII masking? What role do tools like dbt play in ELT? How does schema-on-read versus schema-on-write relate?

ONE CONCRETE EXAMPLE A team landing terabytes of raw JSON event logs into a cloud warehouse loads them as-is, then runs SQL transformations to build clean models on demand. ELT wins here because the warehouse's elastic compute handles the heavy transforms, and keeping raw logs lets analysts rebuild models when definitions change, without re-extracting from source.