https://cloud.google.com/bigquery/docs/access-control#custom_roles
Custom roles enable you to enforce the principle of least privilege, ensuring that the user and service accounts in your organization have only the permissions essential to performing their intended functions.

Creating scheduled snapshots for persistent disk This document describes how to create a snapshot schedule to regularly and automatically back up your zonal and regional persistent disks. Use snapshot schedules as a best practice to back up your Compute Engine workloads. After creating a snapshot schedule, you can apply it to one or more persistent disks.https://cloud.google.com/compute/docs/disks/scheduled-snapshots

Let's evaluate each option based on the requirement of sub-millisecond latency for globally stored IoT data:
A: Spanner with Caching: While Spanner offers strong consistency and global scalability, the base latency might not consistently be sub-millisecond for all read/write operations globally. Introducing caching adds complexity and doesn't guarantee sub-millisecond latency for all initial reads or cache misses.
B: Bigtable: Bigtable is a highly scalable NoSQL database service designed for low-latency, high-throughput workloads. It excels at storing and retrieving large volumes of time-series data, which is typical for IoT sensor data. Its architecture is optimized for single-key lookups and scans, providing consistent sub-millisecond latency, making it a strong candidate for this use case.
C: BigQuery: BigQuery is a fully managed, serverless data warehouse designed for analytical queries on large datasets. While it's excellent for analyzing IoT data in batch, it's not optimized for the low-latency, high- throughput ingestion and retrieval required for real-time IoT applications with sub-millisecond latency needs.
D: Cloud Storage with Cloud CDN: Cloud Storage is object storage and is not designed for low-latency transactional workloads. Cloud CDN is a content delivery network that caches content closer to users for faster delivery, but it's not suitable for the primary storage of rapidly incoming IoT sensor data requiring sub- millisecond write latency.
Google Cloud Documentation References:
Cloud Bigtable Overview: https://cloud.google.com/bigtable/docs/overview - This document highlights Bigtable's suitability for low-latency and high-throughput applications, including IoT. It mentions its ability to handle massive amounts of data with consistent performance.
Spanner Overview: https://cloud.google.com/spanner/docs/overview - While Spanner offers low latency, Bigtable is generally preferred for extremely high-throughput, low-latency use cases like raw sensor data ingestion due to its optimized architecture for such workloads.
BigQuery Overview: https://cloud.google.com/bigquery/docs/introduction - This emphasizes BigQuery's analytical capabilities rather than low-latency operational workloads.
Cloud Storage Overview: https://cloud.google.com/storage/docs/overview - This describes Cloud Storage as object storage, not ideal for sub-millisecond latency reads and writes required for real-time IoT data.