Where to Store Data for Real-Time Weather-Charting Application
Question
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?
Answers
Explanations
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A. B. C. D.C.
Google Cloud Bigtable is a scalable, fully-managed NoSQL wide-column database that is suitable for both real-time access and analytics workloads.
Good for: -> Low-latency read/write access -> High-throughput analytics -> Native time series support Common workloads: -> IoT, finance, adtech -> Personalization, recommendations -> Monitoring -> Geospatial datasets -> Graphs Reference: https://cloud.google.com/storage-options/
To optimize the performance of a real-time weather-charting application that receives data from 50,000 sensors and 10 readings a second, you need to select a storage solution that can handle high volumes of data and provide fast query responses. Among the given options, the most suitable storage solution for this use case is Google Cloud Bigtable (Option C).
Here's why:
Google Cloud Bigtable is a fully managed NoSQL database service that is designed for high throughput and low latency data access. It is a highly scalable and distributed system that can handle petabytes of data with millions of reads and writes per second. This makes it an ideal choice for real-time data-intensive applications, such as the weather-charting application described in the question.
Google Cloud SQL (Option B) is a fully managed relational database service that supports MySQL and PostgreSQL. It is suitable for transactional applications that require ACID compliance and support for SQL queries. However, it may not be the best option for real-time data-intensive applications because it may not be able to handle the high volumes of data and may not provide the required low latency response times.
Google BigQuery (Option A) is a cloud data warehouse that is designed for storing and analyzing large datasets using SQL-like queries. While it can handle massive amounts of data and provides fast query responses, it may not be the best option for real-time data-intensive applications that require sub-second response times.
Google Cloud Storage (Option D) is a highly scalable and durable object storage service that is designed for storing and retrieving large unstructured data objects. While it can handle high volumes of data, it may not be the best option for real-time data-intensive applications that require low latency response times.
In summary, Google Cloud Bigtable is the most suitable storage solution for the given use case because it is optimized for real-time data-intensive applications that require high throughput and low latency data access.
