The resource that you need to get access is in the other project.
roles/bigquery.dataViewer BigQuery Data Viewer
When applied to a table or view, this role provides permissions to:
Read data and metadata from the table or view.
This role cannot be applied to individual models or routines.
When applied to a dataset, this role provides permissions to:
Read the dataset's metadata and list tables in the dataset.
Read data and metadata from the dataset's tables.
When applied at the project or organization level, this role can also enumerate all datasets in the project.
Additional roles, however, are necessary to allow the running of jobs.

Comprehensive and Detailed In Depth Explanation:
The key requirement is to migrate applications that rely on hard-coded internal IP addresses without modifying the application code. To achieve this, the migrated VMs in Google Cloud need to retain their original internal IP addresses.
A: Non-overlapping CIDR ranges and new static IPs: This option requires changing the IP addresses of the migrated workloads, which would necessitate modifying the application code to reflect these new addresses.
This violates a core requirement.
B: Migrating DNS and using ephemeral IPs: While migrating DNS can be beneficial in the long run, using ephemeral internal IP addresses for the migrated workloads means their IPs could change upon restart, breaking the hard-coded IP address dependencies.
C: Single subnet with Cloud NAT and static NAT IP: Cloud NAT allows instances without external IP addresses to access the internet, but it doesn't help in preserving the internal IP addresses that the applications use to communicate with each other. The internal IP addresses of the VMs would still be within the VPC subnet range and might conflict if they are the same as the on-premises IPs.
D: Same CIDR ranges and same static IPs: Creating a VPC with the same CIDR ranges as the on-premises network and assigning the same static internal IP addresses to the migrated workloads is the only way to ensure that the applications can continue to communicate using their hard-coded IP addresses without any code changes. This approach effectively extends the on-premises network's IP address space into Google Cloud (though without direct connectivity initially, as stated in the problem). Once the workloads are migrated, future steps can involve establishing connectivity (e.g., using VPN or Interconnect) if needed for hybrid scenarios.
Google Cloud Documentation References:
VPC Network Overview: https://cloud.google.com/vpc/docs/vpc - This document explains the fundamentals of VPC networks and their IP addressing. While it doesn't explicitly detail lift-and-shift scenarios with identical IP ranges without connectivity, it lays the groundwork for understanding VPC configuration.
Considerations for planning IP address ranges: https://cloud.google.com/vpc/docs/subnets#ip-ranges - This section discusses IP address planning, and while overlapping ranges are generally discouraged for connected networks, for isolated migration scenarios as described, it's a necessary step to avoid application changes. The problem statement explicitly says the environments are not connected during the initial migration.

Let's analyze each option to find the one that meets the requirements of no startup time for new traffic, two idle instances, and minimal administrative overhead:
A: Unchecking "Serve this revision immediately" and using a traffic simulation tool: Unchecking "Serve this revision immediately" does prevent the new revision from receiving traffic immediately. However, manually using a traffic simulation tool adds administrative overhead. It also doesn't guarantee that two idle instances will be ready before traffic is shifted; you would need to monitor and adjust traffic manually based on the simulation.
B: Configuring service autoscaling and setting the minimum number of instances to 2: Service-level autoscaling applies to all revisions of the service. Setting the minimum instances at the service level would ensure at least two instances are running across all active revisions, not specifically for the new revision before traffic shift.
C: Configuring revision autoscaling for the new revision and setting the minimum number of instances to 2:
This is the correct approach. By configuring revision autoscaling specifically for the new revision and setting the minimum number of instances to 2, Cloud Run will ensure that at least two instances of the new version are running and ready to serve traffic before you redirect any traffic to it. This eliminates startup latency when you do shift traffic. It also minimizes administrative overhead as Cloud Run manages the instance scaling based on this configuration.
D: Configuring revision autoscaling for the existing revision and setting the minimum number of instances to
2: This would ensure the existing version has at least two idle instances, which doesn't directly address the requirement of having idle instances ready for the new version before traffic redirection.
Google Cloud Documentation References:
Cloud Run Autoscaling: https://cloud.google.com/run/docs/configuring/min-instances - This document explains how to configure minimum and maximum instances for Cloud Run services and revisions. It clarifies that you can set minimum instances at the revision level to ensure instances are always ready.
Cloud Run Traffic Management: https://cloud.google.com/run/docs/managing/traffic - This describes how to deploy new revisions and gradually shift traffic between them. Combining minimum instances on the new revision with traffic splitting allows for zero-downtime deployments with pre-warmed instances.

Coldline Storage is the perfect service to store audit logs from all the projects and is very cost- efficient as well. Coldline Storage is a very low-cost, highly durable storage service for storing infrequently accessed data.

Keyword need to look for
- "High Throughput",
- "Consistent",
- "Property based data insert/fetch like ngine status, distance traveled, fuel level, and more." which can be designed in column,
- "Large Scale Customer Base + Each Customer has multiple sensor which send event in seconds" This will go for pera bytes situation,
- Export data based on the time of the event.
- Atomic
o BigTable will fit all requirement.
o DataStore is not fully Atomic
o CloudStorage is not a option where we can export data based on time of event. We need another solution to do that o FireStore can be used with MobileSDK.