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.
Besides knowing the centers of a distribution in your data, you need to know how varied the observations are. In this article, we’ll explain how to find the spread of a distribution. Are you dealing with a very uniform or a very spread population? To really understand what the numbers are saying, you must know the answer to this question. In the second part of this series, we discussed how to calculate centers of distribution.
-- -- Previously, we’ve discussed the use of SQL aggregate functions with the GROUP BY statement. Regular readers of the Vertabelo Academy blog will also remember our recent tutorial about JOINs. If you’re a bit rusty on either subject, I encourage you to review them before continuing this article. That’s because we will dig further into aggregate functions by pairing them with JOINs.
Aggregate functions are powerful SQL tools that compute numerical calculations on data, allowing the query to return summarized information about a given column or result set. These functions can be used in conjunction with the GROUP BY statement. Let’s see how they work using some easy examples. SQL Aggregate Functions Suppose we have users residing in a city, and we store their information in two tables. These tables and their relationship are shown below:
As you start coding in SQL, you will use some statements and techniques over and over again. We call these “SQL patterns”. This series will look at the most common SQL patterns and consider how to use them. Previously, we looked at the SQL pattern of matching NULLs. This is important when you are comparing columns containing NULL values. Today, we’re going to consider another SQL practice: conditional summarization with CASE operator.
We’ve already covered how to use the GROUP BY clause and some aggregation functions like SUM(), AVG(), MAX(), MIN(), COUNT(). In this article, we will explain how the GROUP BY clause works when NULL values are involved. We will also explain about using NULLs with the ORDER BY clause. In SQL, NULL is a special marker used to indicate that a data value does not exist in the database. For more details, check out Wikipedia’s explanation of NULL in SQL.