A digital health enterprise is designing a multi-region clinical monitoring architecture on Google Cloud. The solution must fulfill two specific operational requirements:
1. Ingest and store real-time time-series telemetry data emitted by millions of wearable patient devices, requiring single-digit millisecond write latency and high-throughput key-value storage.
2. Store core patient administrative and treatment records requiring strong multi-region ACID compliance, high availability across continents, and ANSI SQL query support.
Which TWO database services should you recommend to meet these requirements? (Select TWO.)
- Cloud Bigtable to handle high-throughput, low-latency time-series IoT device telemetry data.Answer
- Cloud Spanner to host patient records requiring multi-region relational consistency and ANSI SQL compliance.Answer
- CCloud SQL for PostgreSQL with cross-region read replicas to serve multi-region write transactions for patient records.
- DStorage Transfer Service to stream real-time telemetry events directly into cold Cloud Storage buckets.
Answer
The optimal architecture combines Cloud Bigtable for real-time IoT time-series telemetry ingestion and Cloud Spanner for globally consistent, multi-region relational patient records.
Selecting Cloud Bigtable provides the required low latency and massive scale for real-time time-series IoT telemetry. Selecting Cloud Spanner delivers global multi-region transactional consistency with ANSI SQL compatibility for patient record management.
Step-by-Step Solution
Key Concept
Selecting GCP Database Services for Hybrid Workloads (NoSQL Time-Series vs Global Relational ACID)
Estimated Time:2m 0s