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.)
Select 2 answers.
Marked answer: A
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Community Discussion
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Chose C1
A and C This step addresses the cost optimization requirement by moving older data out of the Redshift cluster and into a more cost-effective storage solution like Amazon S3. Redshift Spectrum then allows analytical queries to be run directly against this data in S3, effectively combining it with data still residing in Redshift. This step addresses the requirement to combine live transactional data from Aurora PostgreSQL with current and historical data for analytics. Redshift Federated Query enables Redshift to directly query data in external databases like Aurora PostgreSQL, eliminating the need for complex ETL processes to move this live data into Redshift for analysis.
