3 ways to improve data sampling efficiency in Snowflake

Conditional statements in Snowflake can significantly improve the efficiency of data sampling queries by filtering out irrelevant data and focusing on pertinent information. Three ways to enhance data sampling efficiency include using partitioned tables with sampling, incorporating `CASE` statements for dynamic adjustment of sampling rates or methods based on specific criteria within the data, and utilizing `JOIN` statements to sample data based on relationships between tables or within subsets of a single table. Each method has its own advantages and limitations, and the choice depends on the specific requirements and constraints of the analysis.