AWS Certified Data Engineer Associate DEA-C01 Practice Question
A retail company stores several years of analytics data in Amazon Redshift. The order-processing application writes live transactional data to an Amazon Aurora MySQL cluster. Data engineers need to let analysts run ad-hoc joins between the Aurora tables and Redshift fact tables with near-real-time results but minimal operational overhead and no recurring ETL jobs. Which solution meets these requirements?
Schedule nightly AWS Glue ETL jobs that export Aurora data to Amazon S3 in Parquet and COPY it into Redshift.
Use Amazon Redshift federated queries to create an external schema for the Aurora MySQL cluster and run joins directly from Redshift.
Enable Amazon Redshift Spectrum and register the Aurora tables as external tables through an AWS Glue JDBC crawler.
Set up AWS Database Migration Service (AWS DMS) to continuously replicate Aurora tables into a Redshift staging schema, then query the replicated data.
Amazon Redshift federated queries let Redshift create an external schema that points to Amazon Aurora MySQL. When analysts run a query, Redshift pushes predicates to Aurora, retrieves only the required rows, and joins them with local tables, providing second-level latency without maintaining pipelines. Redshift Spectrum can access only data stored in Amazon S3, so it cannot query Aurora directly. AWS DMS replication or scheduled AWS Glue ETL jobs would copy data into Redshift, adding operational overhead and introducing latency that violates the near-real-time requirement.
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AWS Certified Data Engineer Associate DEA-C01
Data Store Management
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