ETL vs ELT: which approach fits modern cloud data stacks better?
ELT (Extract, Load, Transform) is often preferred in modern cloud data pipelines because cloud warehouses such as BigQuery, Snowflake, and Redshift can perform large-scale data transformations after loading, making it more scalable and cost-effective. ETL shifts transformations ahead of loading into analytic stores and remains useful for some legacy and regulatory scenarios. Cloud-first stacks often adopt ELT because it leverages powerful built-in compute and separates storage from processing.
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