A company's marketing data is uploaded from multiple sources to an Amazon S3 bucket. A series of data preparation jobs aggregate the data for reporting. The data preparation jobs need to run at regular intervals in parallel. A few jobs need to run in a specific order later. The company wants to remove the operational overhead of job error handling, retry logic, and state management. Which solution will meet these requirements?

Questions › Category: SAA-C03 › A company’s marketing data is uploaded from multiple sources to an Amazon S3 bucket. A series of data preparation jobs aggregate the data for reporting. The data preparation jobs need to run at regular intervals in parallel. A few jobs need to run in a specific order later. The company wants to remove the operational overhead of job error handling, retry logic, and state management. Which solution will meet these requirements?

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Admin Staff asked 1 year ago

A company's marketing data is uploaded from multiple sources to an Amazon S3 bucket. A series of data preparation jobs aggregate the data for reporting. The data preparation jobs need to run at regular intervals in parallel. A few jobs need to run in a specific order later.
The company wants to remove the operational overhead of job error handling, retry logic, and state management.
Which solution will meet these requirements?

A. Use an AWS Lambda function to process the data as soon as the data is uploaded to the S3 bucket. Invoke other Lambda functions at regularly scheduled intervals.

B. Use Amazon Athena to process the data. Use Amazon EventBridge Scheduler to invoke Athena on a regular internal.

C. Use AWS Glue DataBrew to process the data. Use an AWS Step Functions state machine to run the DataBrew data preparation jobs.

D. Use AWS Data Pipeline to process the data. Schedule Data Pipeline to process the data once at midnight.








 

Correct Answer: C

This question is in SAA-C03 exam
For getting AWS Certified Solutions Architect Associate Certificate


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