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Answer: Provide a Cloud Storage bucket so that third parties can upload batches of data, and provide appropriate Identity and Access Management (IAM) access to the bucket. Create a Cloud Function with a google.storage.object.finalize Cloud Storage trigger. Write code so that the function can scale up a Compute Engine autoscaling managed instance group. Use an image pre-loaded with the data processing software that terminates the instances when processing completes.
The question revolves around securely processing compute-heavy batches of data with minimal costs and maximum speed. Option A suggests using an SFTP server on a Compute Engine instance, which is less scalable and more complex to manage compared to Cloud Storage. Option B proposes a GKE cluster with two services, which introduces unnecessary complexity and cost for the given scenario. Option D suggests using Cloud Monitoring to trigger a Cloud Function, which is not the most efficient or cost-effective method for processing large batches of data. Option C is the best choice as it leverages Cloud Storage for secure and scalable data uploads, uses a Cloud Function triggered by Cloud Storage events to efficiently scale Compute Engine instances based on demand, and ensures instances are terminated post-processing to minimize costs. This approach optimally balances cost, security, and processing speed.
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Your team is developing a service that handles compute-intensive batch processing, where performance scales with CPU speed and core count. The incoming batch sizes vary and can arrive unpredictably from multiple external sources. You need to securely accept third-party data uploads while minimizing costs and maximizing processing speed. What is the optimal solution?
A
Provide a secure file transfer protocol (SFTP) server on a Compute Engine instance so that third parties can upload batches of data, and provide appropriate credentials to the server. Create a Cloud Function with a google.storage.object.finalize Cloud Storage trigger. Write code so that the function can scale up a Compute Engine autoscaling managed instance group Use an image pre-loaded with the data processing software that terminates the instances when processing completes.
B
Provide a Cloud Storage bucket so that third parties can upload batches of data, and provide appropriate Identity and Access Management (IAM) access to the bucket. Use a standard Google Kubernetes Engine (GKE) cluster and maintain two services: one that processes the batches of data, and one that monitors Cloud Storage for new batches of data. Stop the processing service when there are no batches of data to process.
C
Provide a Cloud Storage bucket so that third parties can upload batches of data, and provide appropriate Identity and Access Management (IAM) access to the bucket. Create a Cloud Function with a google.storage.object.finalize Cloud Storage trigger. Write code so that the function can scale up a Compute Engine autoscaling managed instance group. Use an image pre-loaded with the data processing software that terminates the instances when processing completes.
D
Provide a Cloud Storage bucket so that third parties can upload batches of data, and provide appropriate Identity and Access Management (IAM) access to the bucket. Use Cloud Monitoring to detect new batches of data in the bucket and trigger a Cloud Function that processes the data. Set a Cloud Function to use the largest CPU possible to minimize the runtime of the processing.