The correct option is ELT.

ELT is better suited for very large datasets because it loads raw data directly into scalable storage and relies on the data warehouse or data lake compute to perform transformations. This approach avoids a single transformation bottleneck and lets you scale storage and compute independently so ingestion can grow to meet high volume demands.

ELT also enables faster initial ingestion because raw data can be landed quickly and transformed later using parallel and elastic processing. Many cloud data platforms provide fast bulk loading and separate compute resources which align well with the ELT pattern for high throughput pipelines.

ETL is incorrect because it requires transforming data before loading which can create scaling and performance bottlenecks. Pre load transformation infrastructure must scale with every incoming dataset and that makes it harder to ingest very large volumes quickly.

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