How should a data engineering team choose between ELT and ETL in cloud platforms?

All answers to this question.

The shift to modern cloud data warehouses has made ELT the preferred choice for most scalable data engineering setups. In traditional ETL, transformation happened on an intermediate server before loading, which created severe processing bottlenecks. With cloud infrastructure, it is far more efficient to load raw data directly into the warehouse and leverage its massive distributed computing power to execute transformations. This ensures that your raw log streams are immediately safely preserved, while your downstream business intelligence tools can query clean, transformed views on demand.

   Answered 2025-05-08 by Diana Lawson

Are you planning to implement a data lakehouse pattern to separate your raw ingestion layer from the production business intelligence schemas?

   Answered 2025-06-19 by Scott Fitzgerald

• Yes, we are planning to use an object storage layer as our data lakehouse staging area. This allows us to ingest raw streams instantly using ELT workflows, while keeping our analytical schemas optimized without degrading concurrent query performance.

     Commented 2025-06-22 by Victor Morales

ELT is highly superior for scaling fast, but you must monitor your warehouse compute credits closely since running transformations directly in the cloud warehouse gets expensive quickly.

   Answered 2025-08-14 by Craig Reynolds

• I completely agree. Without strict resource clustering and scheduled query optimization, cloud warehouse costs can rapidly eclipse traditional on-premise operational budgets.

     Commented 2025-08-18 by Jeffrey Lane