You want to optimize the performance of an accurate, real-time, weather-charting application. The data comes from 50,000 sensors sending 10 readings a second, in the format of a timestamp and sensor reading. Where should you store the data?

Google Cloud Bigtable is ideal for this scenario due to its high throughput and low-latency capabilities for large analytical and operational workloads. With 50,000 sensors sending 10 readings per second, the application generates 500,000 data points per second, requiring a database that can handle massive write and read operations efficiently. Bigtable is a NoSQL wide-column store designed for exactly this type of time-series data, making it perfect for real-time charting. Google BigQuery is a data warehouse best suited for large-scale analytical queries, not high-velocity, real-time writes. Google Cloud SQL is a relational database good for transactional workloads but would struggle with the extreme write throughput required here. Google Cloud Storage is an object storage service, not a database, and is unsuitable for direct, real-time data ingestion and querying for an application like this.