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To avoid timeouts during large data uploads with the PUT command in Snowflake, it is recommended to:

Compress files before loading: Compressed files are smaller and upload faster, reducing the risk of timeouts.

Increase the PARALLEL option value: This option allows more simultaneous upload threads, improving upload speed and efficiency for large datasets.

Semi-structured file formats and table staging do not directly impact timeouts, while enabling overwrite does not prevent timeouts but rather controls overwriting of existing files.

In Snowflake, shares are used to share data between Snowflake accounts. When creating a share, it is possible to grant access to the share to roles within the Snowflake account that is creating the share. The type of role that can be granted to a share is a Custom role. Custom roles are user-defined roles that account administrators can create to manage access control in a more granular way. Unlike predefined roles such as ACCOUNTADMIN or SYSADMIN, custom roles can be tailored with specific privileges to meet the security and access requirements of different groups within an organization.

Granting a custom role access to a share enables users associated with that role to access the shared data if the share is received by another Snowflake account. It is important to carefully manage the privileges granted to custom roles to ensure that data sharing aligns with organizational policies and data governance standards.

References:

Snowflake Documentation on Shares: Shares

Snowflake Documentation on Roles: Access Control

Column-level security in Snowflake enhances data protection by restricting access and applying masking policies to sensitive data at the column level.

Limiting Access Based on User Privileges:

Column-level security allows administrators to define which users or roles have access to specific columns within a table.

This ensures that sensitive data is only accessible to authorized personnel, thereby reducing the risk of data breaches.

Application of Masking Policies:

Masking policies can be applied to columns to obfuscate sensitive data.

For example, credit card numbers can be masked to show only the last four digits, protecting the full number from being exposed.

References:

Snowflake Documentation: Column-Level Security

Snowflake Documentation: Dynamic Data Masking