Which approach is best for a one-time copy of a small local file into the lakehouse when convenience is prioritized?

• A. Dataflow Gen2 query
• B. Local file upload by using Lakehouse explorer
• C. Spark notebook
• D. Pipeline with the Copy data activity

Answer: (B)

For a one-time ingestion of a small local file where convenience is the priority, the fastest path is uploading directly from your local machine using Lakehouse explorer. This approach requires no coding or pipeline setup—just select the file and upload, and it becomes available in the lakehouse with minimal steps. Other options introduce more work: a Dataflow Gen2 query is built for transforming and moving data in pipelines, which adds setup and configuration for a simple copy. A Spark notebook involves writing and running code to read the local file and write it to the lakehouse, which is more time-consuming and complex. A pipeline with the Copy data activity typically expects the source to be in cloud storage or a connected data store, so you’d need an intermediate upload step to staging storage first, adding extra steps for a one-off task.