You are choosing a database backend for a new application. The application will ingest data points from IoT sensors. You need to ensure that the application can scale up to millions of requests per second with sub-10ms latency and store up to 100 TB of history. What should you do?
To handle millions of requests per second with sub-10ms latency and store up to 100 TB of historical data, Google Cloud Bigtable is the appropriate choice. Bigtable is a highly scalable, fully managed NoSQL database service designed for large analytical and operational workloads. It efficiently handles high-throughput, low-latency data access and is capable of storing petabytes of data, making it suitable for IoT applications with significant data ingestion and storage requirements.
This is a bigtable use case
Answer is D
IOT -->> Bigtable
C is not a correct answer, D- Bigtable is correct choice for this user case. https://cloud.google.com/memorystore/docs/redis/redis-overview
D. Simply a classic use case for Bigtable. Neither Cloud SQL, Firestore nor Memorystore have either the capacity or latency to provide the solution.
https://cloud.google.com/memorystore/docs/redis/redis-overview#what_its_good_for What it's good for: Stream Processing: Whether processing a Twitter feed or stream of data from IoT devices, Memorystore for Redis is a perfect fit for streaming solutions. Correct answer is C
You just forgot the 100TB of data that needs to be stored 🙏
Correct answer is C
Key is 100TB of historical data. BigTable is the only service (among the mentioned) that is able to handle that load within the required latency.
point to consider is : IOT data, scaling for accepting millions of request. storing historic data of 100TB. Big Table is used for low latency but its for operational workload and we need Fully managed Redis and Memcached for sub-millisecond data access. that's the only argument here. i think option c is correct.
https://medium.com/google-cloud/which-database-should-i-choose-44be039179ea