A company wants to improve the data load time of a sales data dashboard. Data has been collected as .csv files and stored within an Amazon S3 bucket that is partitioned by date. The data is then…

Question

Options

• ACompress .csv files and use an INSERT statement to ingest data into Amazon Redshift.
• BSplit large .csv files, then use a COPY command to load data into Amazon Redshift.
• CUse Amazon Kinesis Data Firehose to ingest data into Amazon Redshift.
• DLoad the .csv files in an unsorted key order and vacuum the table in Amazon Redshift.

• A
  18% (4)
• B
  73% (16)
• C
  5% (1)
• D
  5% (1)

Explanation

Splitting large CSV files and using the COPY command (Option B) is the best approach for high-throughput loading into Amazon Redshift. The COPY command is the recommended and most efficient method for bulk data ingestion into Redshift. When multiple files are provided, Redshift automatically parallelizes the load across all compute slices in the cluster, maximizing I/O throughput. Option A is wrong because INSERT statements are row-by-row operations - extremely slow for 500 GB/day. Option C uses Kinesis Firehose, which is designed for streaming ingestion, not bulk batch loading optimization. Option D is counterproductive - loading in unsorted order increases the need for VACUUM operations, adding overhead rather than improving performance.

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