Which configuration will meet these requirements?
Use AWS Lambda to trigger an AWS Step Functions workflow to wait for dataset uploads to complete in Amazon S3. Use AWS Glue to join the datasets. Use an Amazon CloudWatch alarm to send an SNS notification to the Administrator in the case of a failure.
Develop the ETL workflow using AWS Lambda to start an Amazon SageMaker notebook instance. Use a lifecycle configuration script to join the datasets and persist the results in Amazon S3. Use an Amazon CloudWatch alarm to send an SNS notification to the Administrator in the case of a failure.
Develop the ETL workflow using AWS Batch to trigger the start of ETL jobs when data is uploaded to Amazon S3. Use AWS Glue to join the datasets in Amazon S3. Use an Amazon CloudWatch alarm to send an SNS notification to the Administrator in the case of a failure.
Use AWS Lambda to chain other Lambda functions to read and join the datasets in Amazon S3 as soon as the data is uploaded to Amazon S3. Use an Amazon CloudWatch alarm to send an SNS notification to the Administrator in the case of a failure.
Explanations:
AWS Lambda can trigger an AWS Step Functions workflow to wait for data to be uploaded to Amazon S3. Step Functions can manage the orchestration of the jobs, including checking if the data is available. AWS Glue can be used for joining the datasets, and a CloudWatch alarm can send an SNS notification if the job fails, ensuring proper failure handling.
Using AWS Lambda to start an Amazon SageMaker notebook instance is unnecessary for this ETL process. SageMaker is typically used for machine learning tasks, not for joining large datasets. This option does not efficiently handle the ETL needs described.
AWS Batch is designed for batch processing, and while it can be used to trigger jobs based on S3 events, it’s not an ideal choice here. AWS Glue is more suited for joining large datasets, and using AWS Batch adds unnecessary complexity.
Using AWS Lambda to chain other Lambda functions for reading and joining large datasets is inefficient and could be resource-intensive. Lambda functions are not ideal for handling large datasets (terabytes in size) due to execution time and memory limits, making it an unsuitable choice for this workflow.
I calculate that the answer is:
Use AWS Lambda to trigger an AWS Step Functions workflow to wait for dataset uploads to complete in Amazon S3. Use AWS Glue to join the datasets. Use an Amazon CloudWatch alarm to send an SNS notification to the Administrator in the case of a failure.