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Answer: Deploy Cloud Spanner using a multi-region instance, and place your compute resources close to the default leader region.
The question describes a scenario where a large retail and ecommerce company is migrating to Google Cloud with requirements for scalability, low latency transactions, and a reliable customer experience, while retaining the existing relational schema. Option A suggests using Firestore, which is a NoSQL document database, not suitable for relational schemas and transactional consistency required for sales transactions and inventory levels. Option C proposes an in-memory cache with Memorystore, which is not a persistent storage solution and doesn't meet the requirement for a storage layer. Option D recommends Bigtable, a NoSQL wide-column database, which also doesn't support relational schemas. Option B, deploying Cloud Spanner in a multi-region instance, is the correct choice because Cloud Spanner is a fully managed relational database that offers horizontal scalability, strong consistency, and high availability across regions, making it ideal for global applications requiring low latency and reliable transactions. Placing compute resources close to the default leader region further optimizes performance.
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What is the recommended approach for migrating a large retail and ecommerce company's relational database to Google Cloud, ensuring scalability, low-latency transactions, and reliability while maintaining the existing schema for sales transactions and inventory data?
A
Store your data in Firestore in a multi-region location, and place your compute resources in one of the constituent regions.
B
Deploy Cloud Spanner using a multi-region instance, and place your compute resources close to the default leader region.
C
Build an in-memory cache in Memorystore, and deploy to the specific geographic regions where your application resides.
D
Deploy a Bigtable instance with a cluster in one region and a replica cluster in another geographic region.
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