Diagram
Two side-by-side process flows comparing ETL and ELT. In ETL, data flows from the source to Cloud Storage, is transformed in Dataflow, then loads into BigQuery. In ELT, raw data flows from the source through Cloud Storage into BigQuery first, and SQL transformation happens inside BigQuery afterward, producing transformed tables.
How ETL and ELT Order Changes Data Flow
Data flow order defines the BigQuery storage boundary that separates ETL from ELT. In ETL, data flows from the source system to a transformation engine and only then to BigQuery, so BigQuery mainly holds finished, ready-to-use tables. In ELT, data flows from the source system to Cloud Storage and BigQuery first, and transformation happens later inside BigQuery, 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. The choice therefore depends on whether heavy work should stay outside the warehouse or raw data and SQL work should stay inside it.
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