What is ETL vs ELT and how does this relate to cloud data pipelines?

What is ETL vs ELT and how does this relate to cloud data pipelines?

• A. ETL transforms data before loading into a target; ELT loads data then transforms inside the warehouse/data lake.
• B. ETL loads data into the target first and then transforms; ELT transforms before loading.
• C. ETL and ELT both perform transformations only after data has been loaded to the destination.
• D. ETL transforms data before loading into a target; ELT loads data then transforms inside the warehouse/data lake.

Answer: (D)

The concept tested is where data transformations happen in relation to loading in ETL versus ELT, and how that fits cloud data pipelines. In ETL, you transform the data before loading into the target data store—the cleaning, shaping, and conformity happen in an intermediate process, and only the transformed data is loaded. In ELT, you load the raw data first into the target (data warehouse or data lake) and then perform the transformations inside that target using its own compute resources. This distinction matters in cloud architectures because modern data warehouses offer scalable processing power; ELT leverages that by doing transformations after loading, often enabling faster ingestion and more flexible data modeling. ETL can still be useful when you want to enforce data quality and reduce data volume before landing in the warehouse. The option describing ETL transforming beforehand and ELT transforming inside the warehouse matches these definitions, making it the correct choice.

The concept tested is where data transformations happen in relation to loading in ETL versus ELT, and how that fits cloud data pipelines. In ETL, you transform the data before loading into the target data store—the cleaning, shaping, and conformity happen in an intermediate process, and only the transformed data is loaded. In ELT, you load the raw data first into the target (data warehouse or data lake) and then perform the transformations inside that target using its own compute resources. This distinction matters in cloud architectures because modern data warehouses offer scalable processing power; ELT leverages that by doing transformations after loading, often enabling faster ingestion and more flexible data modeling. ETL can still be useful when you want to enforce data quality and reduce data volume before landing in the warehouse. The option describing ETL transforming beforehand and ELT transforming inside the warehouse matches these definitions, making it the correct choice.