To improve the performance of visualization queries while minimizing maintenance overhead, using materialized views is the most effective solution. Here's why option C is the best choice:
Materialized Views:
Materialized views store the results of a query physically, allowing for faster access compared to regular views which execute the query each time it is accessed.
They can be automatically refreshed to reflect changes in the underlying data.
Incremental Updates:
The incremental updates capability of BigQuery materialized views ensures that only the changed data is processed during refresh operations, significantly improving performance and reducing computation costs.
This feature helps maintain up-to-date data in the materialized view with minimal processing time, which is crucial for data that needs to be no less than 4 hours old.
Performance and Maintenance:
By using materialized views, you can pre-compute and store the results of complex queries involving outer joins and analytic functions, resulting in faster query performance for data visualizations.
This approach also reduces the maintenance overhead, as BigQuery handles the incremental updates and refreshes automatically.
Steps to Implement:
Create Materialized Views:
Define materialized views for the visualization queries with the necessary configurations CREATE MATERIALIZED VIEW project.dataset.view_name AS SELECT ...
FROM ...
WHERE ...
Enable Incremental Updates:
Ensure that the materialized views are set up to handle incremental updates automatically.
Reference in Visualization Tool:
Update the data visualization tool to reference the materialized views instead of running the original queries directly.
Reference Links:
BigQuery Materialized Views
Optimizing Query Performance

To use a Shared VPC network for a Dataflow pipeline, you need to specify the subnetwork parameter with the full URL of the subnetwork, and grant the service account that executes the pipeline the compute.
networkUser role in the host project. This role allows the service account to use the subnetworks in the Shared VPC network. The Dataflow service agent does not need this role, as it only creates and manages the resources for the pipeline, but does not execute it. The dataflow.admin role is not related to the network access, but to the permissions to create and delete Dataflow jobs and resources. References:
* Specify a network and subnetwork | Cloud Dataflow | Google Cloud
* How to config dataflow Pipeline to use a Shared VPC?

Shared VPC requires that you designate a host project to which networks and subnetworks belong and a service project, which is attached to the host project. When Cloud Composer participates in a Shared VPC, the Cloud Composer environment is in the service project.Reference:https://cloud.google.com/composer/docs
/how-to/managing/configuring-shared-vpc