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Difficulty: MediumSelecting and Designing Storage and Database Resources

Match each enterprise data requirement on the left with the most appropriate Google Cloud storage or database service on the right based on access patterns, scaling, and architectural capabilities.

  • Global OLTP application requiring horizontal scalability, strong consistency across multiple geographic regions, and relational SQL support.Cloud Spanner
  • High-throughput IoT sensor data ingestion requiring single-digit millisecond read/write latency and seamless scaling for key-value time-series data.Cloud Bigtable
  • Regional relational database workload needing full PostgreSQL compatibility, ACID compliance, managed backups, and high-availability failover.Cloud SQL for PostgreSQL
  • Enterprise analytical data warehouse capable of querying petabyte-scale structured data using SQL without managing underlying infrastructure.BigQuery

Answer

Global multi-region strong consistency matches Cloud Spanner; high-throughput IoT time-series data matches Cloud Bigtable; regional PostgreSQL relational OLTP matches Cloud SQL for PostgreSQL; petabyte-scale analytics matches BigQuery.
Each GCP database service is optimized for specific workload profiles: Cloud Spanner handles globally scalable relational OLTP; Cloud Bigtable handles massive throughput NoSQL key-value streams; Cloud SQL handles standard regional relational databases; and BigQuery handles large-scale analytics and data warehousing.

Step-by-Step Solution

1
Analyze workload requirements for transactional scope and geographical distribution.
Identified global multi-region transactional needs versus regional single-instance requirements.
Global relational workloads requiring horizontal scale map directly to Cloud Spanner, whereas single-region relational workloads map to Cloud SQL.
2
Differentiate non-relational high-throughput operational storage from analytical warehousing.
Categorized key-value IoT ingestion as operational NoSQL and large SQL reporting as analytical OLAP.
Cloud Bigtable provides low-latency key-value NoSQL capabilities for high-speed write streams, while BigQuery provides serverless OLAP capabilities for petabyte-scale analysis.
3
Pair each requirement to its optimal Google Cloud database product based on best-practice design patterns.
All four workload patterns are mapped to their corresponding Google Cloud target services.
Matching access patterns and SLAs ensures optimal performance, reliability, and cost-efficiency.

Key Concept

Selecting GCP Database and Storage Resources by Access Pattern and Workload Characteristics
Estimated Time:1m 30s
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Topics

Selecting and Designing Storage and Database Resources