Explanation:
The optimal solution involves adding a second cluster with multi-cluster routing and configuring distinct app profiles for live and analytical workloads. This approach offers several advantages:
Alternatives are less suitable for various reasons:
This strategy ensures high performance, scalability, and cost-efficiency while maintaining the reliability of your real-time application.
Ultimate access to all questions.
No comments yet.
You are managing a real-time application on Google Cloud Bigtable that handles a mix of read and write operations under heavy load. A new requirement has emerged to perform hourly analytical computations across the entire database. Your primary goal is to ensure the reliability of your production application while accommodating the analytical workload. Which approach should you take?
A
Increase the size of your current cluster and run the analytics workload on the expanded cluster.
B
Introduce a second cluster to your existing instance with multi-cluster routing. Set up a live-traffic app profile for your regular workload and a batch-analytics profile for the analytical workload.
C
Transfer a copy of the Bigtable data to Google Cloud Storage (GCS) and execute the hourly analytical job on the exported data.
D
Add a second cluster to your existing instance with single-cluster routing. Configure a live-traffic app profile for your regular workload and a batch-analytics profile for the analytical workload.