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Difficulty: HardSelecting and Designing Storage and Database Resources

Match each workload requirement and data access pattern on the left with the most appropriate managed Google Cloud storage or database service on the right.

  • A global transactional order management system requiring multi-region, strongly consistent ACID relational operations with ANSI SQL support and a 99.999% availability SLA.Cloud Spanner
  • A high-velocity IoT fleet monitoring system requiring single-digit millisecond write latency for time-series key-value telemetry data and seamless analytical integration with BigQuery.Cloud Bigtable
  • A cross-platform mobile application requiring serverless hierarchical JSON document storage, real-time client data synchronization, and offline data persistence.Firestore
  • An enterprise media and audit repository storing unstructured video assets and compliance logs with automated cost optimization via object lifecycle management rules.Cloud Storage

Answer

1. Global multi-region relational ACID with 99.999% SLA maps to Cloud Spanner.
2. Low-latency time-series key-value IoT telemetry maps to Cloud Bigtable.
3. Mobile app real-time JSON document sync maps to Firestore.
4. Unstructured media and audit log lifecycle management maps to Cloud Storage.
Each GCP database and storage service addresses specific architectural trade-offs: Cloud Spanner for globally distributed relational ACID workloads; Cloud Bigtable for high-throughput, low-latency key-value/time-series data; Firestore for real-time mobile serverless document synchronization; and Cloud Storage for unstructured object data with lifecycle management.

Step-by-Step Solution

1
Analyze global multi-region relational ACID requirements.
Cloud Spanner is the only relational GCP database service providing multi-region external consistency, ANSI SQL compatibility, and five-nines (99.999%) availability.
Traditional relational databases like Cloud SQL do not provide global multi-region horizontal scaling or 99.999% SLAs.
2
Analyze high-throughput IoT time-series telemetry data requirements.
Cloud Bigtable provides low-latency key-value write throughput suitable for massive IoT streaming datasets and integrates directly with BigQuery.
Relational databases cannot handle massive, continuous single-digit millisecond write loads efficiently without complex sharding.
3
Analyze mobile JSON document synchronization requirements.
Firestore provides serverless document storage with native client SDKs supporting offline persistence and automatic real-time listener updates.
Relational and object storage engines lack native client state synchronization and offline caching primitives.
4
Analyze unstructured object storage and tiering requirements.
Cloud Storage is designed for blob objects like media files and logs, supporting automated transition rules between Nearline, Coldline, and Archive classes.
Database services are inefficient and cost-prohibitive for storing raw binary media files.

Key Concept

Matching workload access patterns, consistency requirements, latency expectations, and data structure types to optimal GCP database and storage services.
Estimated Time:2m 0s
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Topics

Selecting and Designing Storage and Database Resources