Hi @Bindi P, Databricks supports sharing feature tables across multiple workspaces. For example, from your workspace, you can create, write to, or read from a feature table in a centralized feature store. This is useful when multiple teams share access to feature tables or when your organization has numerous workspaces to handle different stages of development.
For a centralized feature store, Databricks recommends that you designate a single workspace to store all feature store metadata and create accounts for each user who needs access to the feature store.
Suppose your teams are also sharing models across workspaces. In that case, you may choose to dedicate the same centralized workspace for both feature tables and models or specify different centralized workspaces for each.
Access to the centralized feature store is controlled by tokens. Each user or script that needs access creates a personal access token in the centralized feature store and copies that token into the secret manager of their local workspace. Each API request sent to the centralized feature store workspace must include the access token; the Feature Store client provides a simple mechanism to specify the secrets to be used when performing cross-workspace operations.
For more information, please go through the documentation.