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Another important technique is using window functions. Window functions allow you to perform calculations across a set of table rows that are related to the current row. Unlike aggregate functions, window functions don't collapse rows; they return a value for each row. Window functions are super useful for tasks like calculating moving averages, ranking items, and calculating running totals. ClickHouse provides a rich set of window functions. For example, the `ROW_NUMBER()` function assigns a unique number to each row within a partition. The `RANK()` function assigns a rank to each row based on the values in a column. And the `LAG()` and `LEAD()` functions allow you to access values from previous or subsequent rows. For example, calculating a 7-day moving average could look something like this:
