You have a Fabric tenant that contains a lakehouse named Lakehouse1. You have an external Snowflake database that contains a table with 200 million rows. You need to use a data pipeline to migrate the

The Copy data activity within a Fabric data pipeline is purpose-built for high-performance, large-scale data ingestion. It leverages optimized connectors and parallel processing capabilities to efficiently transfer massive datasets from various sources, including Snowflake, to a Lakehouse, making it the fastest option for bulk migration.

Dataflow Gen2 is designed for data ingestion and transformation, but for pure high-volume, high-speed data transfer without complex transformations, the Copy data activity typically offers superior performance.

The Lookup activity is designed to retrieve a single row or a small set of configuration data, not to perform bulk ingestion of 200 million rows.

A Spark notebook activity requires writing custom code and introduces Spark cluster overhead, which might be less performant than the highly optimized, native Copy data activity for a straightforward bulk data migration.