This Xplenty white paper compares ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) for data integration. ETL transforms data before loading into a data warehouse, ideal for structured, consistent sources requiring high data quality and security. ELT loads raw data into a target system or data lake, transforming it there, suiting large, varied, or real-time datasets and flexible analytics. It outlines when to use data warehouses (structured, predictable queries) vs. data lakes (unstructured, evolving queries) and factors like real-time needs, structuring, and volatility, emphasizing matching the approach to data type, volume, and business goals.

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