To improve the performance of querying a large BigQuery table with filters on countryname and username, clustering the table by these fields is the most effective approach. Here's why option C is the best choice:
Clustering in BigQuery:
Clustering organizes data based on the values in specified columns. This can significantly improve query performance by reducing the amount of data scanned during query execution.
Clustering by countryname and username means that data is physically sorted and stored together based on these fields, allowing BigQuery to quickly locate and read only the relevant data for queries using these filters.
Filter Efficiency:
With the table clustered by countryname and username, queries that filter on these columns can benefit from efficient data retrieval, reducing the amount of data processed and speeding up query execution.
This directly addresses the performance issue of the dashboard queries that apply filters on these fields.
Steps to Implement:
Redesign the Table:
Create a new table with clustering on countryname and username:
CREATE TABLE project.dataset.new_table
CLUSTER BY countryname, username AS
SELECT * FROM project.dataset.customer_order;
Migrate Data:
Transfer the existing data from the original table to the new clustered table.
Update Queries:
Modify the dashboard queries to reference the new clustered table.
Reference:
BigQuery Clustering Documentation
Optimizing Query Performance

To enable different teams to manage their own data while allowing data exchange across the organization, using Analytics Hub is the best approach. Here's why option C is the best choice:
Analytics Hub:
Analytics Hub allows teams to publish their data as data exchanges, making it easy for other teams to discover and subscribe to the data they need.
This approach maintains each team's control over their data while facilitating easy and secure data sharing across the organization.
Data Publishing and Subscribing:
Teams can publish datasets they control, allowing them to manage access and updates independently.
Other teams can subscribe to these published datasets, ensuring they have access to the latest data without duplicating efforts.
Minimized Operational Tasks and Costs:
This method reduces the need for complex replication or data synchronization processes, minimizing operational overhead.
By centralizing data sharing through Analytics Hub, it also reduces storage costs associated with duplicating large datasets.
Steps to Implement:
Set Up Analytics Hub:
Enable Analytics Hub in your Google Cloud project.
Provide training to teams on how to publish and subscribe to data exchanges.
Publish Data:
Each team publishes their datasets in Analytics Hub, configuring access controls and metadata as needed.
Subscribe to Data:
Teams that need access to data from other teams can subscribe to the relevant data exchanges, ensuring they always have up-to-date data.
Reference:
Analytics Hub Documentation
Publishing Data in Analytics Hub
Subscribing to Data in Analytics Hub