Snowflake's Fail-safe provides a 7-day period during which data in a permanent table may be recoverable, but only by Snowflake Support, not by user operations3.

When a query containing a WHERE clause is running longer than expected, and the Query Profile shows that all micro-partitions are being scanned, the query can be optimized by adding a clustering key to the table.
* Understanding Micro-Partitioning in Snowflake:
* Snowflake automatically partitions tables into micro-partitions for efficient storage and query performance.
* Each micro-partition contains metadata about the range of values it holds, which helps in pruning irrelevant partitions during query execution.
* Role of Clustering Keys:
* A clustering key defines how data in a table is organized within micro-partitions.
* By specifying a clustering key, you can control the physical layout of data, ensuring that related rows are stored together.
* This organization improves query performance by reducing the number of micro-partitions that need to be scanned.
* Optimizing Queries with Clustering Keys:
* Adding a clustering key based on columns frequently used in WHERE clauses helps Snowflake quickly locate and scan relevant micro-partitions.
* This minimizes the amount of data scanned and reduces query execution time.
* Example:
ALTER TABLE my_table CLUSTER BY (column1, column2);
* This command adds a clustering key to my_table using column1 and column2.
* Future queries that filter on these columns will benefit from improved performance.
* Benefits:
* Reduced query execution time: Fewer micro-partitions need to be scanned.
* Improved resource utilization: More efficient data retrieval leads to lower compute costs.
References:
* Snowflake Documentation: Clustering Keys
* Snowflake Documentation: Query Profile

The Enterprise edition of Snowflake allows for a dedicated metadata store, providing additional features designed for large-scale enterprises

Reference: https://docs.snowflake.com/en/sql-reference/sql/get.html