A company needs to read multiple terabytes of data for an initial load as part of a Snowflake migration. The company can control the number and size of CSV extract files.
How does Snowflake recommend maximizing the load performance?
A.
Use auto-ingest Snowpipes to load large files in a serverless model.
B.
Produce the largest files possible, reducing the overall number of files to process.
C.
Produce a larger number of smaller files and process the ingestion with size Small virtual warehouses.
D.
Use an external tool to issue batched row-by-row inserts within BEGIN TRANSACTION and COMMIT commands.
c i think
https://www.analytics.today/blog/top-3-snowflake-performance-tuning-tactics#:~:text=Avoid%20Scanning%20Files&text=Before%20copying%20data%2C%20Snowflake%20checks,that%20have%20already%20been%20loaded.
Split larger files into a greater number of smaller files to distribute the load among the compute resources in an active warehouse. The number of data files that are processed in parallel is determined by the amount of compute resources in a warehouse. We recommend splitting large files by line to avoid records that span chunks.
https://docs.snowflake.com/en/user-guide/data-load-considerations-prepare#:~:text=Split%20larger%20files,that%20span%20chunks.
Snowpipe is designed for continuous ingestion and is built on COPY.
The COPY command enables loading batches of data available in external cloud storage or an internal stage.
So for the initial stage i think that i s a better solution COPY
So the answer is C
upvoted 2 times
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