A company is migrating its database servers from Amazon EC2 instances that run Microsoft SQL Server to Amazon RDS for Microsoft SQL Server DB…

This solution is the most operationally efficient because it uses AWS Glue, a fully managed ETL (extract, transform, and load) service, to perform the entire task in a single, cohesive job. By creating a view in the source SQL Server database, the complex join logic is encapsulated and simplified. The AWS Glue job can then connect directly to the database via JDBC, query the simple view, perform the format conversion to Parquet natively, and write the output to Amazon S3. Scheduling this single job is straightforward and minimizes operational overhead compared to multi-step, multi-service alternatives.

1. AWS Glue Developer Guide - Connecting to a JDBC Data Store: AWS Glue can use a JDBC connection to connect directly to a database

such as SQL Server running on an EC2 instance. The guide states

"You can use AWS Glue to connect to JDBC databases both on-premises and in the AWS Cloud." This supports directly querying the EC2 instance.

2. AWS Glue Developer Guide - Job Authoring: This documentation details how an AWS Glue job can extract data from a source

transform it

and load it into a destination. The transformation includes format conversion. "AWS Glue can write data in Parquet format to Amazon S3." This confirms Glue's native ability to handle the required output format.

3. AWS Glue Developer Guide - Working with partitioned data in AWS Glue: This section explicitly mentions Parquet as a supported format for both reading and writing data

highlighting its integration within the service. "AWS Glue supports reading and writing common data formats like CSV

JSON

Parquet

and Avro."

4. Microsoft Docs - CREATE VIEW (Transact-SQL): "A view can be used to... Encapsulate the complexity of queries with common joins." This official documentation supports the use of a view to simplify the query logic that the AWS Glue job will execute.