QuestionQ290

Data Ingestion and Transformation

A retail company stores data from a product lifecycle management (PLM) application in an on-premises MySQL database. The PLM application updates the database frequently when transactions occur.

The company wants to obtain near-real-time insights from the PLM application. It wants to integrate these insights with other business datasets and analyze the combined dataset by using an Amazon Redshift data warehouse.

The company has already established an AWS Direct Connect connection between the on-premises infrastructure and AWS.

Which solution will satisfy these requirements with the LEAST development effort?

  • A Run a scheduled AWS Glue extract, transform, and load (ETL) job to get the MySQL database updates by using a Java Database Connectivity (JDBC) connection. Set Amazon Redshift as the destination for the ETL job.
  • B Run a full load plus CDC task in AWS Database Migration Service (AWS DMS) to continuously replicate the MySQL database changes. Set Amazon Redshift as the destination for the task.
  • C Use the Amazon AppFlow SDK to build a custom connector for the MySQL database to continuously replicate the database changes. Set Amazon Redshift as the destination for the connector.
  • D Run scheduled AWS DataSync tasks to synchronize data from the MySQL database. Set Amazon Redshift as the destination for the tasks.
Explanation

AWS Database Migration Service (AWS DMS) can use MySQL as a source and Amazon Redshift as a target. A full-load-plus-CDC task copies the existing source data and continuously applies subsequent database changes, providing a managed, near-real-time replication path to the Redshift warehouse with minimal development effort.

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