When encountering a quotaExceeded error in BigQuery, you should follow these steps to diagnose and mitigate the issue:
* Understand the Error:
* The error message indicates that a quota was exceeded (either a short-term rate limit or a longer- term limit).
* The response payload contains information about which quota was reached.
* Quotas can fall into two categories:
* rateLimitExceeded: Short-term limits. Retry the operation after a few seconds using exponential backoff.
* quotaExceeded: Longer-term limits. Wait 10 minutes or longer before retrying the operation.
* Search Errors in Cloud Audit Logs (Option A):
* Cloud Audit Logs provide detailed information about API requests and responses.
* By searching the logs, you can identify the specific API call that triggered the quotaExceeded error.
* This helps you understand which resource or operation exceeded the quota.
* View Errors in Cloud Monitoring (Option C):
* Cloud Monitoring (formerly known as Stackdriver) provides insights into your Google Cloud resources.
* Check the monitoring dashboard for any alerts related to BigQuery quotas.
* You can set up custom monitoring rules to track specific quotas and receive notifications.
* Other Options:
* B. Configure Cloud Trace: Cloud Trace is used for performance analysis and latency tracking. It' s not directly related to quota issues.
* D. Use Information Schema Views: Information schema views provide metadata about your datasets and tables but won't help diagnose quota errors.
* E. Use BigQuery Bl Engine: There is no such tool called "BigQuery Bl Engine." This option is invalid.
Remember that some quotas replenish incrementally over a 24-hour period, so you don't always need to wait a full 24 hours after reaching the limit. If you consistently hit longer-term quotas, consider workload optimization or requesting a quota increase

If your apps are fault-tolerant and can withstand possible instance preemptions, then preemptible instances can reduce your Compute Engine costs significantly. For example, batch processing jobs can run on preemptible instances. If some of those instances stop during processing, the job slows but does not completely stop. Preemptible instances complete your batch processing tasks without placing additional workload on your existing instances and without requiring you to pay full price for additional normal instances.
https://cloud.google.com/compute/docs/instances/preemptible

https://cloud.google.com/iam/docs/understanding-service-
accounts#using_service_accounts_with_compute_engine
https://cloud.google.com/storage/docs/access-control/iam-roles

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.

https://cloud.google.com/monitoring/audit-logging