Fine-Grained Access Control in Snowflake for Data Scientists

Question

Pulumi · Accepted Answer

To set up fine-grained access control in Snowflake for data scientists, one could use a combination of Snowflake's User and Role resources, along with Grants to manage permissions. The idea is to create user accounts for the data scientists, define roles with specific privileges, and then grant those roles to the users. Below is a general explanation of how you might set this up using Pulumi with the Snowflake provider, followed by a Python program that demonstrates the creation of a user, a role, and grants necessary permissions.

1. **Users**: In Snowflake, users represent individual accounts with login capabilities. You define users by specifying usernames, passwords, and other optional properties such as default roles.

2. **Roles**: Roles in Snowflake are collections of access rights. You create roles and grant them the necessary privileges to access different Snowflake objects like databases, schemas, or tables.

3. **Grants**: Snowflake uses a grant-based access control system. Once you create roles, you grant them permissions to perform actions on Snowflake objects. You can make these grants with or without the option for the role to grant permissions to other roles.

Now, let's write the Pulumi program:

```python
import pulumi
import pulumi_snowflake as snowflake

# Create a Snowflake user for a data scientist.
data_scientist_user = snowflake.User("data-scientist-user",
    # Replace these with the actual user details and credentials.
    name="data_scientist",
    login_name="data_scientist_login",
    password="a_strong_password", # It's best to manage the password outside of the code, e.g., using Pulumi Config or a secret store.
    comment="User account for the data scientist",
    default_role="data_scientist_role",
    disabled=False
)

# Create a Snowflake role for data scientists.
data_scientist_role = snowflake.Role("data-scientist-role",
    name="data_scientist_role",
    comment="Role for data scientists with fine-grained permissions"
)

# Grant the role to the user.
user_role_grant = snowflake.UserGrant("user-role-grant",
    user_name=data_scientist_user.name, # The resource reference automatically uses the `name` output property.
    roles=[data_scientist_role.name] # Grant the newly created role to the user.
)

# Define the permissions your data scientists need. This example assumes a specific database and schema.
# You would replace these with actual resource names and grants suitable for your scenario.

# Grant USAGE on a specific database to the data scientist role.
database_usage_grant = snowflake.DatabaseGrant("database-usage-grant",
    roles=[data_scientist_role.name],
    database_name="target_database",
    privilege="USAGE"
)

# Grant USAGE on a specific schema to the data scientist role.
schema_usage_grant = snowflake.SchemaGrant("schema-usage-grant",
    roles=[data_scientist_role.name],
    schema_name="target_schema",
    database_name="target_database",
    privilege="USAGE"
)

# Grant SELECT on specific tables within a schema to the data scientist role.
table_select_grant = snowflake.TableGrant("table-select-grant",
    roles=[data_scientist_role.name],
    tables=[{
        "database_name": "target_database",
        "schema_name": "target_schema",
        "name": "target_table"
    }],
    privilege="SELECT"
)

# Export the user and role names
pulumi.export("data_scientist_username", data_scientist_user.name)
pulumi.export("data_scientist_role_name", data_scientist_role.name)
```

In this program, we:
- Create a user account for a data scientist, assigning them a default role that we'll be defining next.
- Define a new role with a name and description that indicates it is for data scientists.
- Grant the custom role to the user account.
- Provide USAGE grants on a specific database and schema, ensuring that the role only has the necessary privileges.
- Provide SELECT grants on specific tables within the schema, which lets the role read data without giving broader permissions.

Please make sure to replace placeholder values with actual Snowflake resource names and credentials, and take care to securely manage secrets like passwords. This scaffolding provides the groundwork for fine-grained access control—you would continue adding grants as needed for your specific data access policies.

Remember, Pulumi's infrastructure as code approach allows you to define and manage these access control settings in versioned code, making changes easy to track and audit.