AWS Certified Data Engineer Associate DEA-C01 Practice Question
A company runs an on-premises PostgreSQL database that stores 10 years of sales data. The data engineering team must migrate several large historical tables, including their indexes, partitions, and user-defined functions, to Amazon Redshift. After the initial migration, new rows added to the source tables must automatically appear in Redshift with minimal manual intervention. Which solution will meet these requirements?
Use AWS Schema Conversion Tool to convert the PostgreSQL schema to Amazon Redshift, then configure AWS Database Migration Service for full load and ongoing replication.
Run an AWS Glue crawler to infer the PostgreSQL schema and launch Glue jobs that write Parquet files into Redshift on a schedule.
Export the tables to CSV files in Amazon S3, create Redshift external tables with Redshift Spectrum, and schedule nightly COPY commands into local tables.
Create Amazon Athena federated queries to access the PostgreSQL tables and build views in Redshift that reference those queries.
AWS Schema Conversion Tool (AWS SCT) converts PostgreSQL schemas, indexes, partitions, and procedural code to the equivalent objects in Amazon Redshift. After the conversion, AWS Database Migration Service (AWS DMS) can perform full-load plus ongoing replication to keep the target cluster synchronized with new changes in the source. This combination provides one-time schema conversion and continuous data movement with little operational overhead.
Exporting CSV files to Amazon S3 and running periodic COPY or Spectrum queries requires manual orchestration and does not migrate procedural code. AWS Glue crawlers only discover schema; they cannot convert user-defined functions or maintain continuous replication. Athena federated queries do not migrate data or objects into Redshift and therefore do not satisfy the requirement.
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AWS Certified Data Engineer Associate DEA-C01
Data Store Management
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