Comprehensive and Detailed Explanation:
This question describes a classic, scalable streaming analytics architecture on Google Cloud.
Option C is the correct answer as it combines the best-in-class managed services for each part of the pipeline.
Pub/Sub is a fully managed, highly scalable messaging service perfect for ingesting fluctuating volumes of streaming IoT data.
Dataflow is a fully managed, serverless service for stream and batch processing that automatically scales resources up and down to handle fluctuating data volumes.
BigQuery is a serverless, highly scalable data warehouse optimized for analytics.
Cloud KMS is the standard Google Cloud service for creating and managing your own cryptographic keys, which are then used to enable Customer-Managed Encryption Keys (CMEK) across services like Pub/Sub, Dataflow, and BigQuery, satisfying the security requirement.
Option A is incorrect because it specifies using a Google-managed encryption key, which violates the requirement for encryption with "your own key." Option B is incorrect because Cloud SQL is a relational database (OLTP), not an analytical data warehouse (OLAP), making it unsuitable for storing and analyzing large volumes of streaming data. BigQuery is the appropriate choice.
Option D is incorrect because Dataproc is a managed Hadoop/Spark service. While powerful, it is less
"managed" and serverless than Dataflow, as you still need to provision and manage clusters. For a fully elastic and managed solution, Dataflow is the preferred choice.
Reference (Google Cloud Documentation Concepts):
This architecture is a canonical pattern for streaming analytics on Google Cloud. The "Streaming analytics" solution guide frequently highlights the Pub/Sub -> Dataflow -> BigQuery pattern. Each of these services supports Customer-Managed Encryption Keys (CMEK) using keys from Cloud KMS, ensuring data is protected at rest with customer-controlled keys, which aligns with the principle of customer control over data security.