ETL vs ELT: Where Transform Happens

Evaluating ETL and ELT means deciding where transformation happens relative to BigQuery storage. In extract, transform, load (ETL), data is cleaned and reshaped before it is stored in BigQuery using outside compute such as Dataflow. In extract, load, transform (ELT), raw data is loaded into BigQuery first and transformed afterward inside BigQuery with SQL.

Data flow order defines the BigQuery storage boundary that separates the two patterns. In ETL, data flows from the source system to a transformation engine and only then to BigQuery, so BigQuery mainly holds finished tables. In ELT, data flows from the source system to Cloud Storage and BigQuery first, so BigQuery holds both raw and transformed tables. Cloud Storage is the shared staging dependency in both patterns, ideally in the same location as the BigQuery dataset to minimize latency.