PDE Storing the Data Practice Question
A company is migrating an on-premises PostgreSQL database to Google Cloud. The database runs complex analytical queries mixed with OLTP workloads. They need PostgreSQL compatibility and want to improve analytical query performance without changing the application. Which database should they choose?
⚠ Common exam trap
Many candidates choose Cloud SQL for PostgreSQL because it is the most familiar PostgreSQL option within Google Cloud, overlooking that AlloyDB is Google's specialized service for mixed OLTP and analytical workloads, providing PostgreSQL compatibility with built-in analytical acceleration. Cloud SQL lacks these advanced analytical features.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
AlloyDB
AlloyDB is the correct choice because it is a fully managed PostgreSQL-compatible database service specifically designed for demanding transactional and analytical workloads. It combines the PostgreSQL ecosystem with a columnar engine and adaptive caching to accelerate analytical queries by up to 100x over standard PostgreSQL, all without requiring application changes. This makes it ideal for mixed OLTP and complex analytical queries while maintaining PostgreSQL compatibility.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
BigQuery
Why it's wrong here
BigQuery is a serverless columnar analytics warehouse that does not accept standard PostgreSQL wire-protocol connections or run the application's OLTP statements, so the application would need rewriting. It is tempting because it excels at complex analytical queries, and would be correct if only analytics, not mixed OLTP, were required.
- ✗
Cloud Spanner
Why it's wrong here
Cloud Spanner uses its own GoogleSQL and PostgreSQL-dialect engines with distributed sharding, not full PostgreSQL compatibility, and its row-based storage does not accelerate analytical scans. It is tempting for globally distributed OLTP at scale, and would be correct if horizontal scalability and strong global consistency were the priority.
- ✗
Cloud SQL for PostgreSQL
Why it's wrong here
Cloud SQL for PostgreSQL runs the standard PostgreSQL engine, so analytical queries execute on the same row-store architecture as on-premises and gain no columnar acceleration. It is tempting because it preserves PostgreSQL compatibility with minimal migration effort, and would be correct if only compatibility and managed operations were required.
- ✓
AlloyDB
Why this is correct
AlloyDB provides full PostgreSQL wire compatibility, so the application connects unchanged, while its columnar engine accelerates analytical scans and aggregates alongside standard row-based OLTP processing. This directly satisfies the stem's requirement to improve analytical query performance on mixed workloads without application modification.
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