Best Practices for Data Loading into Snowflake
Instruction: Describe the best practices for efficiently loading data into Snowflake.
Updated
Official answer
Choose the ingestion path from volume, latency and source format: bulk COPY for staged files, Snowpipe for continuous file arrival, or a supported row-streaming path when that fits the requirement.
Prepare well-formed files with explicit format options and predictable paths. Balance file parallelism against overhead and latency; Snowflake's file-sizing guidance is a starting point, not a rule that Parquet or a larger warehouse always wins. Preserve complete records when splitting files. A stage stores or references files; it does not automatically cleanse them.
Use a warehouse sized by measured bulk-load throughput. Adding multi-cluster concurrency is not a general way to accelerate one load. Validate types, rejected rows, row counts and source totals before publishing the resulting dataset.
Design retries deliberately. COPY load history helps avoid reloading known files, but it has retention and scope limits; FORCE can reload data and create duplicates. Use stable source identities and reconciliation when replay requirements exceed the built-in history behavior.
Set an explicit error policy. Multiple loads inside a transaction are committed together only when the workflow handles failures correctly; a failed statement does not necessarily force the entire transaction to roll back. Stop and roll back on a batch failure instead of blindly committing later successful statements. Avoid unexpected DDL transaction boundaries.
Measure end-to-end freshness, throughput, errors and total cost, and test duplicate files, malformed records and interrupted runs.