Explanation
Some aggregate queries are answered thru micro partitions metadata only not requiring any VW spin ups.

Storing non-native values, such as dates and timestamps, in a VARIANT column in Snowflake can lead to slower query performance and increased storage consumption. VARIANT is a semi-structured data type that allows storing JSON, AVRO, ORC, Parquet, or XML data in a single column. When non-native data types are stored as VARIANT, Snowflake must perform implicit conversion to process these values, which can slow down query execution. Additionally, because the VARIANT data type is designed to accommodate a wide variety of data formats, it often requires more storage space compared to storing data in native, strongly-typed columns that are optimized for specific data types.
The performance impact arises from the need to parse and interpret the semi-structured data on the fly during query execution, as opposed to directly accessing and operating on optimally stored data in its native format.
Furthermore, the increased storage consumption is a result of the overhead associated with storing data in a format that is less space-efficient than the native formats optimized for specific types of data.
References:
* Snowflake Documentation on Semi-Structured Data: Semi-Structured Data

The layer of Snowflake's architecture associated with virtual warehouses is the Query Processing layer. Virtual warehouses in Snowflake are dedicated compute clusters that execute SQL queries against the stored data.
This layer is responsible for the entire query execution process, including parsing, optimization, and the actual computation. It operates independently of the storage layer, enabling Snowflake to scale compute and storage resources separately for efficiency and cost-effectiveness.
References:
* Snowflake Documentation: Snowflake Architecture

In Snowflake, a task allows a user to execute a single SQL statement/command using a predefined schedule (B). Tasks are used to automate the execution of SQL statements at scheduled intervals.