A global logistics provider is architecting the storage layer for a real-time fleet telema… — Google Cloud Professional Cloud Architect

A global logistics provider is architecting the storage layer for a real-time fleet telematics and dynamic routing platform on Google Cloud. The system must fulfill three distinct operational requirements:

1. High-throughput, low-latency ingest of time-series GPS and vehicle telemetry ( $100,000+$ writes per second) with access patterns focused on vehicle ID and timestamp range queries.
2. A relational datastore for operational vehicle inventory, driver assignments, and active route state requiring single-region strong ACID transactions and standard SQL compliance.
3. Cost-effective long-term archiving of raw trip logs that are accessed infrequently for annual compliance audits, with automated transitions to cold storage after 30 days.

Which combination of Google Cloud storage and database services should the solution architect recommend to meet all technical requirements while minimizing cost and operational overhead?

Cloud Bigtable for time-series vehicle telemetry, Cloud SQL for operational relational fleet data, and Cloud Storage with Object Lifecycle Management for trip log archiving.Answer
B
Cloud Bigtable for time-series vehicle telemetry, Cloud Spanner for operational relational fleet data, and Cloud Storage with Object Lifecycle Management for trip log archiving.
C
Cloud Bigtable for time-series vehicle telemetry, Cloud SQL with Customer-Supplied Encryption Keys (CSEK) for operational relational fleet data, and Storage Transfer Service with Transfer Appliance for automated 30-day cold archiving.
D
Cloud Bigtable for time-series vehicle telemetry, Cloud SQL using Customer-Supplied Encryption Keys (CSEK) managed in Cloud KMS for operational relational fleet data, and Cloud Storage for trip log archiving.

Answer

The optimal architecture combines Cloud Bigtable for high-throughput time-series telemetry ingest, Cloud SQL for single-region relational operational fleet data, and Cloud Storage with Object Lifecycle Management for automated cold archiving of historical logs.

The combination of Cloud Bigtable, Cloud SQL, and Cloud Storage with Object Lifecycle Management precisely aligns each workload requirement with its optimal GCP storage service while minimizing cost and operational complexity. Cloud Bigtable excels at high-speed time-series ingestion; Cloud SQL provides robust single-region relational ACID capabilities; and Cloud Storage lifecycle rules automate cost-optimized log archiving.

Step-by-Step Solution

Analyze requirement 1: High-throughput write telemetry ($100k+ writes/sec) with time-series queries.

Identify Cloud Bigtable as the ideal service for high-write-volume NoSQL time-series data indexed by composite row keys (Vehicle ID + Timestamp).

Cloud Bigtable scales linearly for massive write ingestion and low-latency key-range lookups.

Analyze requirement 2: Relational operational data requiring single-region strong ACID compliance.

Select Cloud SQL (PostgreSQL/MySQL) over Cloud Spanner.

Cloud SQL satisfies single-region relational ACID requirements cost-effectively, whereas Cloud Spanner is intended for globally distributed horizontal scaling.

Analyze requirement 3: Long-term unstructured log archiving with automated 30-day transition to cold storage.

Choose Cloud Storage with Object Lifecycle Management policies.

Lifecycle rules natively automate class transitions (e.g., Standard to Nearline/Coldline/Archive) based on object age without operational intervention.

Key Concept

Mapping multi-component application data patterns (high-throughput time-series, single-region relational transactional, and cold blob archiving) to native GCP storage services.

Estimated Time:2m 0s

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Step-by-Step Solution

Key Concept

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