Which solution will meet these requirements MOST cost-effectively?
Configure the AWS Data Exchange product as a producer for an Amazon Kinesis data stream. Use an Amazon Kinesis Data Firehose delivery stream to transfer the data to Amazon S3. Run an AWS Glue job that will merge the existing business data with the Athena table. Write the result set back to Amazon S3.
Use an S3 event on the AWS Data Exchange S3 bucket to invoke an AWS Lambda function. Program the Lambda function to use Amazon SageMaker Data Wrangler to merge the existing business data with the Athena table. Write the result set back to Amazon S3.
Use an S3 event on the AWS Data Exchange S3 bucket to invoke an AWS Lambda function. Program the Lambda function to run an AWS Glue job that will merge the existing business data with the Athena table. Write the results back to Amazon S3.
Provision an Amazon Redshift cluster. Subscribe to the AWS Data Exchange product and use the product to create an Amazon Redshift table. Merge the data in Amazon Redshift. Write the results back to Amazon S3.
Explanations:
Using Kinesis Data Streams and Firehose introduces additional complexity and cost, as it involves real-time data streaming and potentially higher costs for data transfer and processing, which may not be necessary for a batch process. The overall time to complete the task might exceed the 30-60 minutes requirement due to the added latency from streaming.
While using an S3 event to invoke a Lambda function is a good approach, Lambda has execution time limits that may not suit the merging process, especially if the dataset is large. Additionally, SageMaker Data Wrangler is not typically used for simple data merging tasks and may introduce unnecessary costs and complexity.
This option effectively utilizes an S3 event to trigger a Lambda function, which can manage the execution of an AWS Glue job to merge the datasets. AWS Glue is designed for ETL tasks and can handle larger datasets efficiently, meeting the 30-60 minutes requirement without unnecessary costs.
Provisioning an Amazon Redshift cluster is costly and time-consuming compared to other options. It also requires the additional step of creating tables and merging data within Redshift, which can complicate the process and potentially exceed the time limit for completion.
I gauge that the answer is:
Use an S3 event on the AWS Data Exchange S3 bucket to invoke an AWS Lambda function. Program the Lambda function to run an AWS Glue job that will merge the existing business data with the Athena table. Write the results back to Amazon S3.