SOL-C01 · Question #172
A Snowflake data engineer is tasked with optimizing the performance of daily data loads into a fact table named `SALES FACT. The data is loaded from multiple CSV files stored in an AWS S3 bucket. The
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Question
A Snowflake data engineer is tasked with optimizing the performance of daily data loads into a fact table named `SALES FACT. The data is loaded from multiple CSV files stored in an AWS S3 bucket. The data engineer observes that the data loading process is slow, even though the virtual warehouse is adequately sized. Analyzing the COPY INTO command, they notice that data loading happens in single thread even though multiple files are staged. Which of the following techniques, or combination of techniques, would be MOST effective in parallelizing the data load and improving the overall loading performance?
Options
- AIncrease the size of the virtual warehouse to the largest available size (e.g., X-Large or higher).
- BEnsure that the CSV files are split into smaller files (e.g., 10-100MB each). Snowflake
- CUse Snowpipe with auto-ingest enabled to continuously load data as new files arrive in the S3
- DPartition the 'SALES FACT table by a relevant date column and then use the 'ON ERROR =
- EB and C are both correct
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