Snowflake provides a powerful set of analytical functions that allow you to perform advanced calculations and aggregations over partitions or windows of data. These functions are used to analyze and derive insights from your data. Here are some commonly used analytical functions in Snowflake:

1. ROW_NUMBER: Assigns a unique number to each row within a result set.

Example: **`ROW_NUMBER() OVER (ORDER BY column)`** assigns a unique number to each row based on the order of a column.

2. RANK: Assigns a rank to each row within a result set, with ties receiving the same rank.

Example: **`RANK() OVER (ORDER BY column)`** assigns a rank to each row based on the order of a column.

3. DENSE_RANK: Assigns a dense rank to each row within a result set, with ties receiving the same rank, but leaving no gaps in ranks.

Example: **`DENSE_RANK() OVER (ORDER BY column)`** assigns a dense rank to each row based on the order of a column.

4. LAG: Accesses the value of a previous row within a result set.

Example: **`LAG(column, n)`** retrieves the value of the column from the previous row, where n specifies the number of rows back.

5. LEAD: Accesses the value of a subsequent row within a result set.

Example: **`LEAD(column, n)`** retrieves the value of the column from the next row, where n specifies the number of rows ahead.

6. NTILE: Divides the result set into a specified number of equally sized groups and assigns a group number to each row.

Example: **`NTILE(n) OVER (ORDER BY column)`** divides the rows into n groups based on the order of a column.

7. FIRST_VALUE: Returns the value of the specified expression from the first row within a result set.

Example: **`FIRST_VALUE(column) OVER (ORDER BY column)`** retrieves the value of the column from the first row.

8. LAST_VALUE: Returns the value of the specified expression from the last row within a result set.

Example: **`LAST_VALUE(column) OVER (ORDER BY column)`** retrieves the value of the column from the last row.

9. SUM: Calculates the sum of a column within a specified window or partition.

Example: **`SUM(column) OVER (PARTITION BY partition_column)`** calculates the sum of the column within each partition.

10. AVG: Calculates the average of a column within a specified window or partition.

Example: **`AVG(column) OVER (PARTITION BY partition_column)`** calculates the average of the column within each partition.

These are just a few examples of the analytical functions available in Snowflake. Analytical functions help in performing calculations and analysis on data within partitions or windows, allowing for advanced data analysis and deriving meaningful insights. The Snowflake documentation provides a comprehensive list of analytical functions with detailed explanations and usage examples.