Google Associate Cloud EngineerPlanning and configuring a cloud solutionHard
A startup is deploying a new IoT platform that will ingest millions of small, high-throughput data points from devices globally. The data needs to be stored for real-time analytics and machine learning. The data is time-series in nature and requires very low-latency reads and writes. Which Google Cloud storage option is best suited for this workload?
- ACloud Storage
- BCloud SQL
- CCloud Spanner
- DCloud Bigtable
Show answer & explanationAnswer & explanation
Correct answer: D. Cloud Bigtable
Cloud Bigtable is a fully managed, NoSQL wide-column database service that is ideal for large analytical and operational workloads, including IoT time-series data. It offers extremely high throughput and low latency for reads and writes, making it perfect for ingesting millions of data points and serving real-time analytics and machine learning.
Why the other options are wrong
- A. Cloud Storage is object storage, suitable for large files but not for high-throughput, low-latency, small-record reads and writes characteristic of time-series IoT data.
- B. Cloud SQL is a relational database and is not designed for the extremely high throughput and low-latency requirements of millions of small IoT data points.
- C. Cloud Spanner is a globally distributed relational database, suitable for transactional consistency at global scale, but Bigtable is generally optimized for higher throughput and lower latency on time-series and large analytical datasets.
Cloud Bigtable
A fully managed, NoSQL wide-column database service designed for large analytical and operational workloads, offering high throughput and low latency for massive datasets.
- Ideal for time-series data, IoT, and operational analytics.
- Extremely high read/write throughput and low latency.
- Scales to petabytes of data.
Memory trick: Bigtable handles big time-series data with speed.