Cloud Digital Leader Practice Question: Google Cloud products, services, and solutions
A power utility company collects electricity meter readings from 10 million smart meters every 15 minutes — generating billions of rows of time-series data per year. They need to query this data to detect anomalies and patterns. Which Google Cloud database is optimized for this massive-scale time-series IoT data?
⚠ Common exam trap
Watch out — candidates often confuse 'time-series data' with 'relational data' and choose Cloud SQL (PostgreSQL) for its SQL familiarity, overlooking the need for massive horizontal scalability and high write throughput that only Bigtable provides.
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, making it ideal for ingesting and querying high-throughput time-series data from millions of IoT devices. It supports sub-10ms latency on queries, automatic sharding, and seamless integration with Google Cloud's data analytics ecosystem (e.g., BigQuery, Dataflow), which is critical for detecting anomalies and patterns across billions of rows of meter readings.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Cloud SQL (PostgreSQL)
Why it's wrong here
Cloud SQL (PostgreSQL) is a relational OLTP database built for transactional workloads with ACID guarantees, but it cannot absorb the sustained write volume of 10 million smart meters each generating 96 readings per day—nearly a billion rows daily. Even with partitioned tables and connection pooling, PostgreSQL's row-based storage and index maintenance become a bottleneck, and it lacks native time-series compaction or downsampling features. This workload demands a horizontally scalable NoSQL system with distributed writes, not a single-node or limited-region relational engine.
- ✓
Cloud Bigtable
Why this is correct
Cloud Bigtable is a fully managed, wide-column NoSQL database purpose-built for massive time-series workloads just like this—10 million meters × 96 readings/day yields ~960 million rows per day, and Bigtable can ingest millions of writes per second. By designing the row key as `meter_id` + `timestamp`, all readings for a meter are stored contiguously, enabling sub-millisecond range scans for anomaly detection. It stores data as unstructured key-value pairs, scales to petabytes seamlessly, and integrates with BigQuery for analytics, making it the clear choice.
- ✗
Firestore
Why it's wrong here
Firestore is a document-oriented NoSQL database tuned for mobile/web apps with per-document write limits (approximately 1 write per second per document) and no efficient sequential range scans over a billion rows. To store 960 million readings per day, you'd either exhaust Firestore's write throughput or fragment documents, then still struggle to query across meter IDs and timestamps. Its query engine is designed for flexible filters, not the dense, ordered time-series access pattern that Bigtable's row-key-based scans excel at.
- ✗
Cloud Storage (CSV files)
Why it's wrong here
Cloud Storage is object storage, not a queryable database, so storing CSV files would merely park the IoT data without enabling real-time anomaly detection or interactive range scans. Each meter write as a separate object would create billions of tiny files, overwhelming object metadata and making list operations and later loading into BigQuery extremely slow. You would still need a separate query engine to read the CSVs, adding latency and failing the sub-second access pattern the smart meter application requires.
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Cloud Digital Transformation
Key term
Google Cloud
Google Cloud is a suite of cloud computing services offered by Google that provides infrastructure, platform, and software solutions over the internet.
Key term
Bigtable
Bigtable is Google's fully managed, scalable NoSQL database service designed for large analytical and operational workloads, handling petabytes of data with low latency.
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JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This GCDL 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 GCDL exam.