Reference: https://docs.snowflake.com/en/user-guide/data-failsafe.html
Separate and distinct from Time Travel, Fail-safe ensures historical data is protected in the event of a system failure or other catastrophic event, e.g. a hardware failure or security breach. Fail safe feature cannot be enabled or disabled from the user end .

Snowflake's governance features vary across different editions, but theOBJECT_DEPENDENCIESview is supported by all Snowflake editions. This feature is part of Snowflake's Information Schema and is designed to help users understand the dependencies between various objects in their Snowflake environment.
TheOBJECT_DEPENDENCIESview provides a way to query and analyze the relationships and dependencies among different database objects, such as tables, views, and stored procedures. This is crucial for governance, as it allows administrators and data engineers to assess the impact of changes, understand object relationships, and ensure proper management of data assets.
Object tags, masking policies, and row access policies are more advanced features that offer fine-grained data governance capabilities such as tagging objects for classification, dynamically masking sensitive data based on user roles, and controlling row-level access to data. These features may have varying levels of support across different Snowflake editions, with some features being exclusive to higher-tier editions.
Reference: Snowflake Documentation on Information Schema
(https://docs.snowflake.com/en/sql-reference/info-schema/object_dependencies.html)

When using the COPY INTO <table> command to load CSV data from a stage into a Snowflake table, one of the crucial guidelines to follow is that the number of columns in each row within the CSV file should be consistent. This ensures data integrity and allows for a smooth data loading process, as Snowflake expects each row in the CSV file to map directly to a row in the target table based on the number of columns. If there's a mismatch in the number of columns between any row in the file and the target table, Snowflake might return an error or produce unexpected results during the load operation.
It's important to note that while the CSV field delimiter can be specified to something other than a comma, ensuring the consistency in the number of columns across all rows is fundamental to successfully loading data.
Reference:
Snowflake Documentation on Loading Data: Loading CSV Data

In Snowflake's architecture, the Cloud Services layer is responsible for generating the query execution plan. This layer handles all the coordination, optimization, and management tasks, including query parsing, optimization, and compilation into an execution plan that can be processed by the Compute layer.