When loading CSV files using COPY INTO, it's important to note that row order is not guaranteed. This is because the process leverages Spark’s distributed architecture, which reads and processes data in parallel across different nodes. That parallelism can lead to rows being ingested in a different sequence than they appear in the original file.

If maintaining the exact row order is critical for your use case, a reliable solution is to include an explicit ordering column—such as a row_number—in the CSV before loading. After ingestion, you can sort the data based on that column to accurately reconstruct the original sequence.

This approach ensures consistency, especially when working with downstream transformations that depend on the initial row arrangement.