Positional SQL window functions deal with data’s location in the set. In this post, we explain LEAD, LAG, and other positional functions. SQL window functions allow us to aggregate data while still using individual row values. We’ve already dealt with ranking functions and the use of partitions. In this post, we’ll examine positional window functions, which are extremely helpful in reporting and summarizing data. Specifically, we’ll look at LAG, LEAD, FIRST_VALUE and LAST_VALUE. It is worthwhile mentioning that LEAD mirrors …

# Tag: SQL for advanced

You’ve started your mastery of SQL window functions by learning RANK, NTILE, and other basic functions. In this article, we will explain how to use partitions with ranking functions. Mastering SQL window (or analytical) functions is a bumpy road, but it helps to break the journey into logical stages that build on each other. In the previous Common SQL Functions article, you learned about the various ranking functions, which are the most basic form of window functions. In this article, we …

Interested in how SQL window functions work? We use some simple examples to get you started. SQL window functions are a bit different; they compute their result based on a set of rows rather than on a single row. In fact, the “window” in “window function” refers to that set of rows. Window functions are similar to aggregate functions, but there is one important difference. When we use aggregate functions with the GROUP BY clause, we “lose” the individual rows. …

In descriptive statistics, the first four population moments include center, spread, skewness, and kurtosis or peakedness of a distribution. In this article, I am explaining the third and fourth population moments, the skewness and the kurtosis, and how to calculate them. Mean uses the values on the first degree in the calculation; therefore, it is the first population moment. Standard deviation uses the squared values and is therefore the second population moment. Skewness is the third, and kurtosis is the …