You work for a telecommunications service and internet provider company that has been in business for decades. Over the decades, the company has built various types of application systems and database technologies on the evolving platforms of the time. Therefore, you have massive amounts of customer and company operational data on legacy mainframe systems and their associated data stores, such as aging relational databases.
Your team is attempting to build a machine learning model to use streaming data from the company’s in-home routers, functioning as IoT (Internet of Things) devices, and use that data to help the company sell additional services to its customer base. The IoT data is unstructured, so you need to transform it to CSV format before ingesting it into the S3 buckets you use to house your datasets for your SageMaker model. You also need to enrich the IoT data with real-time data from your legacy mainframe systems as the data streams into your AWS cloud environment.
Which set of Amazon services would you use to set up this data transformation and ingestion pipeline?

Answer options:

A.Use Kinesis Data Firehose to receive the streaming data from the IoT devices. Use the Kinesis Data Firehose lambda integration capability to enrich the IoT data with your legacy mainframe systems data and transform it to CSV before writing it to the S3 bucket used by your SageMaker model.
B.Have your legacy mainframe systems write to S3 and use AWS Storage Gateway to enrich the IoT data with your legacy system data and transform it to CSV before writing it to the S3 bucket used by your SageMaker model.
C.Have your legacy mainframe systems write to AWS Storage Gateway using the File Gateway configuration via an NFS (Network File System) connection. Use Kinesis Data Firehose to receive the streaming data from the IoT devices. Use the Kinesis Data Firehose lambda integration capability to enrich the IoT data with your legacy mainframe systems data and convert it to CSV before writing it to the S3 bucket used by your SageMaker model.
D.Use AWS Snowball to migrate your legacy mainframe data to your AWS account. Use Kinesis Data Firehose to receive the streaming data from the IoT devices. Use the Kinesis Data Firehose lambda integration capability to enrich the IoT data with your legacy mainframe systems data and convert it to CSV before writing it to the S3 bucket used by your SageMaker model.

Answer: C
Option A is incorrect. You can’t enrich your IoT data with your mainframe data without first getting your mainframe data into your AWS cloud environment.
Option B is incorrect. You can’t write directly from your mainframe systems to S3. You could use AWS Storage Gateway to get your mainframe data into your AWS cloud environment, but AWS Storage Gateway doesn’t have the capability to enrich your IoT data.
Option C is correct. You can use AWS Storage Gateway using the File Gateway configuration via an NFS (Network File System) connection to move your data from your legacy mainframe systems into your AWS cloud environment. You can then use Kinesis Data Firehose lambda integration to enrich the IoT data with your legacy mainframe systems data and convert it to CSV. Finally, you can have your lambda function write your transformed data to your S3 bucket used by your SageMaker model.
Option D is incorrect. AWS Snowball moves data from your on-premises environment to your AWS cloud environment in a one-time batch. This wouldn’t work since you need real-time integration of your legacy data with your IoT data.
Reference:
Please see the AWS whitepaper titled Building Big Data Storage Solutions (Data Lakes) for Maximum Flexibility.