A company wants to combine data from multiple software as a service (SaaS) applications for analysis. A data engineering team needs to use Amazon…

The most operationally efficient solution is to use Amazon AppFlow, a fully managed integration service designed specifically for transferring data between SaaS applications and AWS services. AppFlow provides pre-built connectors that handle authentication, API interactions, and data transfer, eliminating the need for custom code development and maintenance required by Lambda-based solutions. By scheduling flows in AppFlow to populate an Amazon S3 bucket, the data engineering team can automate the extraction process with minimal operational overhead. AWS Glue can then catalog this data for easy querying by Amazon QuickSight.

1. Amazon AppFlow Documentation: "Amazon AppFlow is a fully managed integration service that enables you to securely transfer data between Software-as-a-Service (SaaS) applications... and AWS services like Amazon S3... in just a few clicks... With AppFlow

you can run data flows on-demand

at a schedule... without having to write any custom code." This directly supports the operational efficiency of option C.

Source: AWS Documentation

"What Is Amazon AppFlow?"

https://docs.aws.amazon.com/appflow/latest/userguide/what-is-appflow.html

2. AWS Lambda Documentation: "AWS Lambda is a serverless

event-driven compute service that lets you run code for virtually any type of application or backend service without provisioning or managing servers." This highlights that Lambda is for running custom code

which implies development and maintenance effort

making it less operationally efficient for this specific use case than a purpose-built service.

Source: AWS Documentation

"What is AWS Lambda?"

https://docs.aws.amazon.com/lambda/latest/dg/welcome.html

3. Amazon Athena Documentation: "To query any data source with Athena

you use an Athena data source connector... A data source connector is a piece of code that can translate between your target data source and Athena." This confirms that option B requires deploying and managing code (the connector)

which is less efficient than AppFlow's managed approach.

Source: AWS Documentation

"Connecting to data sources"

https://docs.aws.amazon.com/athena/latest/ug/connect-to-a-data-source.html

4. Amazon QuickSight Documentation: "You can upload data files

such as Microsoft Excel... This approach is best for smaller datasets that you want to quickly visualize." This positions file uploads (Option D) as a method for quick

small-scale tasks

not for building an efficient

automated data pipeline from multiple SaaS sources.

Source: AWS Documentation

"Creating datasets from new data sources"

https://docs.aws.amazon.com/quicksight/latest/user/create-a-data-set.html