Answer: Write votes to a Pub/Sub topic and toad into both Bigtable and BigQuery via a Dataflow pipeline Query Bigtable for real-time results and BigQuery for later analysis Shutdown the Bigtable instance when voting concludes

## Explanation Option C is the correct solution because it provides: - **Real-time processing**: Using Pub/Sub for streaming data ingestion and Bigtable for real-time querying of voting results - **Batch processing**: Using Dataflow to load data into BigQuery for historical analysis and reporting - **Cost optimization**: Shutting down the Bigtable instance when voting concludes to avoid unnecessary costs - **Scalability**: Pub/Sub and Dataflow can handle high-volume streaming data efficiently **Why other options are incorrect:** - **Option A (Memorystore)**: Redis-based Memorystore is not ideal for large-scale voting data and lacks the analytical capabilities of BigQuery - **Option B (Cloud Functions + BigQuery only)**: Missing real-time processing capabilities and the ability to handle high-volume streaming data efficiently - **Option D (Cloud SQL)**: Relational databases like PostgreSQL are not optimized for high-volume streaming data and real-time analytics at scale This solution leverages Google Cloud's serverless and managed services to create a scalable, cost-effective voting system that supports both real-time and batch processing requirements.

Author: LeetQuiz .