For migrating a 200 million row table from an external Snowflake database into Lakehouse1, which ingestion method is most performant?

For migrating a 200 million row table from an external Snowflake database into Lakehouse1, which ingestion method is most performant?

• A. Dataflow Gen2
• B. Copy data via SQL
• C. Data Pipeline (Copy data)
• D. Azure Data Factory

Answer: (C)

When loading a very large table with no transformation, the goal is to maximize bulk transfer throughput with minimal overhead. A dedicated bulk copy path does just that: it reads the source in parallel chunks and writes to the destination in large blocks, avoiding extra processing or orchestration overhead. The Copy data option in a Data Pipeline is built for this exact scenario, delivering high-throughput, parallelized data movement from Snowflake to Lakehouse1 with minimal transforms. Other approaches introduce more overhead or limit parallelism. A Spark-based Dataflow Gen2 introduces transformation compute and scheduling overhead, which can slow bulk loads. Copying data via SQL can bottleneck if parallelism isn’t exposed or fully leveraged, adding steps and potential latency. While Azure Data Factory is a powerful orchestrator, its raw bulk-load performance depends on the integration runtime and pipeline design, and may not match a specialized copy-path for simple bulk ingestion. So, for a straight bulk load of 200 million rows, the most performant choice is the Copy data option in the Data Pipeline.

When loading a very large table with no transformation, the goal is to maximize bulk transfer throughput with minimal overhead. A dedicated bulk copy path does just that: it reads the source in parallel chunks and writes to the destination in large blocks, avoiding extra processing or orchestration overhead. The Copy data option in a Data Pipeline is built for this exact scenario, delivering high-throughput, parallelized data movement from Snowflake to Lakehouse1 with minimal transforms.

Other approaches introduce more overhead or limit parallelism. A Spark-based Dataflow Gen2 introduces transformation compute and scheduling overhead, which can slow bulk loads. Copying data via SQL can bottleneck if parallelism isn’t exposed or fully leveraged, adding steps and potential latency. While Azure Data Factory is a powerful orchestrator, its raw bulk-load performance depends on the integration runtime and pipeline design, and may not match a specialized copy-path for simple bulk ingestion.

So, for a straight bulk load of 200 million rows, the most performant choice is the Copy data option in the Data Pipeline.