PDE Storing the Data Practice Question
A company needs to store petabytes of time-series IoT sensor data and query it with single-digit millisecond latency at millions of reads per second. The data has a simple key-value structure with timestamps. Which Google Cloud database is MOST appropriate?
⚠ Common exam trap
A common trap in Google exams is to choose Cloud Spanner for structured data, but Spanner is optimized for strong consistency and transactional workloads, not for high-throughput time-series data at petabyte scale with millions of reads per second. BigQuery is for analytical queries, not real-time key-value access. Bigtable's key-value model and low-latency high-throughput design make it the correct choice.
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
✓
Cloud Bigtable
Cloud Bigtable is a fully managed, scalable NoSQL database designed for large analytical and operational workloads, handling petabytes of data with consistent single-digit millisecond latency for high-throughput reads and writes. Its key-value model with timestamp-based versioning is ideal for time-series IoT sensor data, and it supports millions of reads per second via its HBase API and Bigtable's underlying tablet-based architecture.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Firestore
Why it's wrong here
Firestore is a document database with per-document read scaling and higher-latency queries, so it cannot sustain millions of reads per second at single-digit millisecond latency over petabytes. It is tempting for its real-time sync and mobile-friendly SDKs, so it would be correct for user-facing app data with moderate throughput, not IoT time-series at this scale.
- ✗
Cloud Spanner
Why it's wrong here
Cloud Spanner is a strongly consistent relational database with horizontal scaling, but its per-row transactional overhead and higher query latency make it unsuitable for single-digit millisecond key-value reads at millions per second. It is tempting for globally consistent OLTP workloads, so it would be correct for relational transactions needing scale, not this time-series pattern.
- ✓
Cloud Bigtable
Why this is correct
Cloud Bigtable is a wide-column NoSQL store engineered for petabyte scale and consistent single-digit-millisecond reads at millions of operations per second on row-key lookups, matching the key-value timestamped workload. Spanner and BigQuery cannot meet that latency.
- ✗
BigQuery
Why it's wrong here
BigQuery is an analytics warehouse optimised for large-scale SQL scans, not millions of point reads per second at single-digit millisecond latency; its slot-based query model cannot meet that throughput. It is tempting because it genuinely excels at storing and aggregating petabytes of time-series data, which would suit batch analytics rather than the low-latency key-value lookups required here.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This PDE practice question is part of Courseiva's free Google Cloud certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the PDE exam.