A retail company uses Amazon Aurora PostgreSQL to process and store live transactional data. The company uses an Amazon Redshift cluster for a data warehouse. An extract, transform, and load (ETL) job runs every morning to update the Redshift cluster with new data from the PostgreSQL database. The company has grown rapidly and needs to cost optimize the Redshift cluster. A data engineer needs to create a solution to archive historical data. The data engineer must be able to run analytics queries that effectively combine data from live transactional data in PostgreSQL, current data in Redshift, and archived historical data. The solution must keep only the most recent 15 months of data in Amazon Redshift to reduce costs. Which combination of steps will meet these requirements? (Choose two.)

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A. Configure the Amazon Redshift Federated Query feature to query live transactional data that is in C. Schedule a monthly job to copy data that is older than 15 months to Amazon S3 by using the

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B. Configure Amazon Redshift Spectrum to query live transactional data that is in the PostgreSQL

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D. Schedule a monthly job to copy data that is older than 15 months to Amazon S3 Glacier Flexible

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E. Create a materialized view in Amazon Redshift that combines live, current, and historical data

A retail company uses Amazon Aurora PostgreSQL to process and store live transactional data. The company uses an Amazon Redshift cluster for a data warehouse. An extract, transform, and load (ETL) job

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