AWS Certified Data Engineer Associate DEA-C01 Practice Question
A data engineering team needs to move 10 TB of historical fact data from an Amazon Redshift table to Amazon S3 so the cluster can be resized. The data must remain queryable later through Amazon Redshift Spectrum and should be stored in a compressed, columnar format to minimize storage costs. Which solution meets these requirements while using the least additional AWS services?
Extract the rows with the Redshift Data API, store them as compressed CSV files in S3, and catalog the files with an AWS Glue crawler.
Use AWS Database Migration Service to perform a full load from the Redshift table to an S3 target endpoint configured for Parquet.
Run an UNLOAD command with the PARQUET option to export the query result to an S3 prefix, then create an external table that points to the Parquet files.
Run a COPY command to copy the rows into a staging table stored on S3, then query the staging table with Redshift Spectrum.
The UNLOAD command can export the result of a SQL query directly from Amazon Redshift to Amazon S3. When the PARQUET option is specified, Redshift writes compressed columnar Parquet files that are efficient for storage and analytics. Because the files land in S3, an external table in the AWS Glue Data Catalog can reference them for Redshift Spectrum queries without re-loading the data. COPY, DMS, or custom extract jobs introduce unnecessary steps or write row-oriented text formats that waste space and perform poorly with Spectrum.
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AWS Certified Data Engineer Associate DEA-C01
Data Store Management
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