Reference:
premium-for-storing-semi-structured-data

Snowflake Time Travel enables accessing historical data (i.e. data that has been changed or deleted) at any point within a defined period. It serves as a powerful tool for performing the following tasks:
Restoring data-related objects (tables, schemas, and databases) that might have been accidentally or intentionally deleted.
Duplicating and backing up data from key points in the past.
Analyzing data usage/manipulation over specified periods of time.
https://docs.snowflake.com/en/user-guide/data-time-travel.html

To address a high volume of queries queuing in a warehouse, Snowflake recommends two best practices:
* A. Use multi-clustered warehousing to scale out warehouse capacity: This approach allows for the distribution of queries across multiple clusters within a warehouse, effectively managing the load and
* reducing the queue volume.
* B. Scale up the warehouse size to allow Queries to execute faster: Increasing the size of the warehouse provides more compute resources, which can reduce the time it takes for queries to execute and thus decrease the number of queries waiting in the queue.
These strategies help to optimize the performance of the warehouse by ensuring that resources are scaled appropriately to meet demand.
References:
* Snowflake Documentation on Multi-Cluster Warehousing
* SnowPro Core Certification best practices