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Cloud Digital Leader Google Cloud Products and Services 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?

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 designed for exactly this use case — petabyte-scale, low-latency (single-digit ms), high-throughput NoSQL storage for time-series, IoT, and financial data. It scales horizontally by adding nodes. BigQuery is optimised for analytics (seconds-to-minutes latency), Cloud SQL is for OLTP (limited to tens of thousands of QPS), and Firestore is for document data with hierarchical structure.

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-oriented NoSQL database optimized for mobile/web apps that require real-time synchronization, offline support, and hierarchical data models. It offers document-level transactions and rich queries, but its architecture is not built for the append-heavy, high-ingestion patterns of IoT time-series workloads. Scaling to millions of QPS would require sharding that Firestore's per-document write limits cannot sustain, and it lacks time-series-specific features like row-key-based range scans or efficient downsampling.

  • Cloud Spanner

    Why it's wrong here

    Cloud Spanner is a globally distributed, horizontally scaling relational database that provides strong consistency and ACID transactions across regions. While it can handle high throughput, the coordination required for distributed transactions and partitioned splits introduces latency and write amplification that make single-digit millisecond point reads at IoT scale impractical. Spanner is designed for transactional workloads like banking or inventory systems, not for the simple, high-volume key-based lookups that time-series data demands.

  • BigQuery

    Why it's wrong here

    BigQuery is a serverless, columnar data warehouse built for analytical queries over massive datasets, with query execution times typically measured in seconds due to full scans and shuffle operations. It is not a low-latency operational store; even with clustering, partitioning, or streaming inserts, it cannot serve millions of point lookup queries per second for individual sensor readings. Its ingestion model is batch-oriented, and while it excels at aggregations, it is the wrong choice for real-time, single-key access patterns.

  • Cloud Bigtable

    Why this is correct

    Cloud Bigtable is Google's fully managed, wide-column NoSQL database, designed specifically for large-scale analytical and operational workloads like time-series and IoT. It stores data as a sorted key-value map, so row-key range scans over timestamps are extremely fast, and each node supports tens of thousands of reads/writes per second, scaling linearly to millions of QPS. Bigtable also offers time-series-friendly features such as automatic compaction, garbage collection based on age, and time-based row key design, making it the ideal choice for petabytes of sensor data.

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