AmazonAmazon Data Engineer Associate DEA-C01 Certification·EN·Updated 2 Aug 2026
A company is migrating SQL Server databases from EC2 to Amazon RDS for SQL Server. Until migration completes, the analytics team must export large, joined data elements daily in Apache Parquet format to Amazon S3. Which solution is the MOST operationally efficient?
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Correct answer: Create a view in the EC2 instance-based SQL Server databases that contains the required data elements. Create an AWS Glue job that selects the data directly from the view and transfers the data in Parquet format to an S3 bucket. Schedule the AWS Glue job to run every day..
Why this is the answer
This solution is most operationally efficient because AWS Glue is designed for large-scale data extraction, transformation, and loading (ETL) tasks, including direct connections to relational databases and native support for Parquet format. Creating a view simplifies the data selection for Glue, and scheduling the Glue job automates the daily export.
Incorrect options:
Scheduling SQL Server Agent to output CSVs and then using Lambda for transformation adds an unnecessary step and complexity. Lambda is not ideal for large-scale data transformations due to its execution limits.
Using an AWS Glue crawler on a view is not necessary for direct data extraction; crawlers are typically used to infer schema from data sources, not to read views directly for ETL.
An AWS Lambda function for querying large datasets and transforming to Parquet is inefficient. Lambda has memory and execution time limits, making it unsuitable for large data volumes.