Google Cloud Digital LeaderDigital transformation with Google CloudHard
A large manufacturing company is implementing IoT sensors across its production lines to collect real-time operational data. They need a robust, scalable solution to ingest millions of data points per second, process them in real-time for anomaly detection, and store them for long-term analytics. Which Google Cloud services would form the most appropriate pipeline for this scenario?
- ACloud SQL, Compute Engine, and Cloud Storage
- BApp Engine, Cloud CDN, and Cloud Spanner
- CCloud Pub/Sub, Dataflow, and BigQuery
- DCloud Functions, Firestore, and Cloud Load Balancing
Show answer & explanationAnswer & explanation
Correct answer: C. Cloud Pub/Sub, Dataflow, and BigQuery
Cloud Pub/Sub can ingest millions of IoT messages per second. Dataflow is ideal for real-time processing and anomaly detection on streaming data. BigQuery provides a highly scalable data warehouse for long-term storage and complex analytics, completing the real-time IoT data pipeline.
Why the other options are wrong
- A. Cloud SQL is for structured relational data, Compute Engine for VMs, and Cloud Storage for objects; not optimized for real-time streaming analytics.
- B. App Engine for web apps, Cloud CDN for content delivery, and Cloud Spanner for transactional databases are not for real-time IoT data processing.
- D. Cloud Functions for event-driven snippets, Firestore for NoSQL documents, and Cloud Load Balancing for traffic are not suitable for high-volume streaming IoT analytics.
IoT Data Pipeline on Google Cloud
A common pattern for ingesting, processing, and analyzing high-volume, real-time data from IoT devices using Google Cloud services.
- Pub/Sub for message ingestion.
- Dataflow for streaming data processing and transformation.
- BigQuery for scalable data warehousing and analytics.
Memory trick: Publish data, Flow it, Query it big.