AWS Certified Data Engineer Associate DEA-C01 Practice Question
A company stores operational data in an Amazon RDS for PostgreSQL instance and maintains an Amazon Redshift cluster for analytics. Data engineers must allow analysts to join current transactional tables with fact tables that already reside in Redshift, with minimal latency and without creating additional ETL pipelines or duplicating the data. Which approach meets these requirements?
Deploy an Amazon MSK cluster to capture change data from RDS and stream it into Redshift using Amazon Kinesis Data Firehose.
Enable Redshift Spectrum and use AWS Glue jobs to copy the RDS tables to Amazon S3 on a frequent schedule, then create external tables for querying.
Configure Amazon Redshift federated queries to connect to the RDS PostgreSQL instance and create views that reference the remote tables.
Set up AWS Database Migration Service with ongoing replication to load the RDS tables into staging tables in Redshift and merge them during queries.
Amazon Redshift federated queries let Redshift act as a client to an external Aurora PostgreSQL or Amazon RDS for PostgreSQL database. After configuring a database link and IAM permissions, analysts can run standard SQL that joins Redshift tables with the live RDS tables through late-binding or regular views. Because the data remains in RDS and is accessed on demand, no separate replication, S3 staging, or streaming pipeline is required, satisfying the requirement for minimal latency and zero duplication. Redshift Spectrum cannot query RDS; it is designed for data that is already in Amazon S3. Continuous loads with AWS DMS or streaming change data through MSK would meet the analytics need but both replicate the data into Redshift, contradicting the stated constraint.
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AWS Certified Data Engineer Associate DEA-C01
Data Store Management
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