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?
Cloud Bigtable is right solution and correct database choice, which provides high throughput, low latency and scalability for time series data such as this case.
1. High Write Throughput: Bigtable excels at handling high-volume write operations, which is crucial for your application receiving data from 50,000 sensors sending 10 readings per second.
2. Low Latency: Bigtable offers very low latency for read operations, essential for real-time charting and data visualization.
3. Time-Series Data: Bigtable is well-suited for storing and querying time-series data, like your weather sensor readings with timestamps.
4. Scalability: Bigtable can handle massive amounts of data and scale seamlessly as your application grows.
To optimize the performance of an accurate, real-time, weather-charting application that receives data from 50,000 sensors sending 10 readings per second, it would be most appropriate to store the data in a distributed, horizontally scalable, NoSQL database such as Google Cloud Bigtable
Other options, such as Google BigQuery, Google Cloud SQL, and Google Cloud Storage, may not be as well-suited for handling high volumes of real-time data and may not provide the same level of performance and scalability as Google Cloud Bigtable.
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