Google Professional Cloud Developer Question 406
Single answerGoogle Cloud PlatformYou are building a microservices-based application on Google Cloud. One of the microservices needs to write large volumes of time-series data and allow efficient querying over time. Which datastore should you choose to meet these requirements and why?
- A
Google BigQuery
- B
Cloud SQL
- C
Cloud Spanner
- D
Cloud Bigtable
Show answer and explanation
Correct answer: D
Explanation
Cloud Bigtable is specifically designed for large-scale, low-latency workloads, making it the optimal choice for time-series data storage and queries. Its architecture supports high-throughput writes and efficient querying, which aligns well with the requirements outlined in the scenario. Other options like BigQuery, Cloud SQL, and Cloud Spanner serve different use cases and are not as well-suited for time-series data.
- A. Incorrect.
Google BigQuery is designed for analytics and not optimized for high-throughput writes or time-series data. Although it can store large datasets, it is not the best choice for time-series operations.
- B. Incorrect.
Cloud SQL is a relational database service best suited for OLTP workloads and general-purpose relational data storage. It is not optimized for handling large-scale time-series data efficiently.
- C. Incorrect.
Cloud Spanner is a globally distributed relational database designed for highly transactional workloads that require strong consistency. While it supports large data volumes, it is not optimized for time-series use cases involving high-throughput writes and queries over time.
- D. Correct.
Cloud Bigtable is an ideal choice for time-series data because it is highly scalable, supports high-throughput writes, and is optimized for querying over specific ranges of data, such as time ranges.