Q70 — AWS SAA-C03 Ch.10

Question 70 of 100 | ← Chapter 10

Q670. 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. Afew 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?

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

Explanation

To meet the requirements of removing the operational overhead of job error handling, retry logic, and state management for data preparation jobs that aggregate marketing data uploaded to an Amazon S3 bucket, the solution that will best meet these requirements is:C. Use AWS Glue DataBrew to process the data. Use an AWS Step Functions state machine to run the DataBrew data preparation jobs.Here's the rationale behind this solution:Option C: Use AWS Glue DataBrew to process the data. Use an AWS Step Functions state machine to run the DataBrew data preparation jobs.AWS Glue DataBrew is a visual data preparation tool that simplifies the process of cleaning and transforming data. It provides a user-friendly interface to define and execute data preparation tasks. By using AWS Glue DataBrew, you can remove the operational overhead of job error handling, retry logic, and state management, as it provides built-in capabilities for handling these aspects.AWS Step Functions is a serverless workflow service that allows you to coordinate multiple AWS services into a workflow. By using an AWS Step Functions state machine, you can define the sequence and dependencies of the DataBrew data preparation jobs. This enables you to run the jobs in a specific order and handle error handling and retry logic automatically.Option 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. While AWS Lambda can be used to process the data as soon as it is uploaded and invoke other Lambda functions at scheduled intervals, it does not provide the same level of visual data preparation capabilities as AWS Glue DataBrew. Additionally, managing job dependencies, error handling, and retry logic can be more complex and require additional manual effort.Option B: Use Amazon Athena to process the data. Use Amazon EventBridge Scheduler to invoke Athena on a regular interval.Amazon Athena is a serverless query service that allows you to analyze data directly from Amazon S3. While it can be used for querying and processing the data, it does not provide the same level of visual data preparation capabilities as AWS Glue DataBrew. Additionally, using Amazon EventBridge Scheduler to invoke Athena on a regular interval may not provide the same level of control and dependency management as an AWS Step Functions state machine.Option D: Use AWS Data Pipeline to process the data. Schedule Data Pipeline to process the data once at midnight.AWS Data Pipeline is an orchestration service for managing data workflows. While it can be used to schedule data processing at specific times, it may not provide the same level of visual data preparation capabilities as AWS Glue DataBrew. Additionally, running the data preparation jobs only once at midnight may not meet the requirement of running the jobs at regular intervals.Therefore, the solution that will best meet the requirements of removing operational overhead for job error handling, retry logic, and state management for data preparation jobs is to use AWS Glue DataBrew to process the data and use an AWS Step Functions state machine to run the DataBrew data preparation jobs (Option C). This combination provides a visual data preparation tool with built-in error handling and retry logic, while allowing you to define job dependencies and run the jobs in a specific order.