Window Function ROWS and RANGE on Redshift and BigQuery

Jiří Mauritz Data Warehouse, Redshift, Window Functions

Frames in window functions allow us to operate on subsets of the partitions by breaking the partition into even smaller sequences of rows. SQL provides syntax to express very flexible definitions of a frame. We described the syntax in the first post on Window functions and demonstrated some basic use cases in the post on Data Exploration with Window Functions ...

Redshift's Window Functions Advanced use case - Sessionization

Jiří Mauritz Data Warehouse, Redshift, Window Functions

In the last post about the Window Functions, we introduced an advanced use case, in which the window functions help to make the query more readable, simple and efficient. The problem was to find free call intervals for each customer, which are created as customers tops-up their credit for at least €20 and get free calls for the next 28 ...

Redshift Window Functions Advanced Use Cases

Jiří Mauritz Data Warehouse, Redshift, Window Functions

Merging time-based events into periods Thanks to our previous posts about the window functions, Introduction to Window functions on Redshift and Data exploration with Window functions on Redshift, you should now be familiar with the most common functions that can be used in the OVER clause and how to apply them to your data. Today, we introduce more advanced use ...

Data Exploration with Window Functions on Redshift

Jiří Mauritz Data Warehouse, Redshift, Window Functions

We have already introduced the main concept, syntax and simple examples of window functions applied to practical problems. In this post, we will go through some more advanced window functions and aim our focus on analytical use cases. The dataset we will work with consists of information about phone calls and internet usage of two users. For each call, we ...