Snowflake's governance features vary across different editions, but the OBJECT_DEPENDENCIES view 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.
The OBJECT_DEPENDENCIES view 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)

A clustering key can be defined when a table is created by appending a CLUSTER Where each clustering key consists of one or more table columns/expressions, which can be of any data type, except GEOGRAPHY, VARIANT, OBJECT, or ARRAY https://docs.snowflake.com/en/user-guide/tables-clustering-keys

Snowpark-optimized virtual warehouses in Snowflake are designed to efficiently handle workloads with large memory requirements. Snowpark is a developer framework that allows users to write code in languages like Scala, Java, and Python to process data in Snowflake. Given the nature of these programming languages and the types of data processing tasks they are typically used for, having a virtual warehouse that can efficiently manage large memory-intensive operations is crucial.
Understanding Snowpark-Optimized Virtual Warehouses:
Snowpark allows developers to build complex data pipelines and applications within Snowflake using familiar programming languages.
These virtual warehouses are optimized to handle the execution of Snowpark workloads, which often involve large datasets and memory-intensive operations.
Large Memory Requirements:
Workloads with large memory requirements include data transformations, machine learning model training, and advanced analytics.
These operations often need to process significant amounts of data in memory to perform efficiently.
Snowpark-optimized virtual warehouses are configured to provide the necessary memory resources to support these tasks, ensuring optimal performance and scalability.
Other Considerations:
While Snowpark can handle other types of workloads, its optimization for large memory tasks makes it particularly suitable for scenarios where data processing needs to be done in-memory.
Snowflake's ability to scale compute resources dynamically also plays a role in efficiently managing large memory workloads, ensuring that performance is maintained even as data volumes grow.
Snowflake Documentation: Introduction to Snowpark
Snowflake Documentation: Virtual Warehouses