Question

A data engineer must load 200 large CSV files from an Azure Data Lake Storage Gen2 container into a Fabric Lakehouse Delta table on a nightly schedule. The files require no transformation—they must be landed exactly as-is into the table with the highest possible throughput and lowest compute overhead. Which approach should the engineer choose within a Fabric data pipeline?

Accepted Answer

Use a Copy activity in the pipeline to move the files directly into the Lakehouse table

Answer

Use a Dataflow Gen2 with staging enabled to read and write each file to the table

Answer

Use a Notebook activity running PySpark to read each CSV individually and append rows

Answer

Use a KQL query in an Eventhouse to ingest the CSV files into the Lakehouse table