Diagram
A decision tree starting from Cloud Storage staging that branches on whether raw data can land first to choose Dataflow ETL outside BigQuery or BigQuery ELT with SQL transforms.
How to Choose Between Dataflow ETL and BigQuery ELT
Choosing between the two strategies means comparing transformation location, data flow, and processing fit. ETL with Dataflow keeps heavy work outside the warehouse, while ELT with BigQuery keeps raw data and SQL work inside the warehouse. Cloud Storage is the shared staging dependency in both patterns because BigQuery batch loads, Dataflow reads, and offline or network transfers all land files there first.
| Strategy | Transformation location | Data flow | Choose when |
|---|---|---|---|
| ETL with Dataflow | Outside BigQuery in Dataflow | Source to Cloud Storage to Dataflow to BigQuery | Transformations are complex, enrichment is heavy, or warehouse load must be reduced |
| ELT with BigQuery | Inside BigQuery after loading | Source to Cloud Storage to BigQuery to transformed tables | Raw data should land fast and SQL can handle cleaning and aggregation |
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