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 ...

Introduction to Window Functions on Redshift

Jiří Mauritz Data Warehouse, Redshift, Window Functions

Benefits of Window Functions Window functions on Redshift provide new scope for traditional aggregate functions and make complex aggregations faster to perform and simpler to write. Window functions also allow the SQL developer to look across rows and perform inter-row calculations. The main benefits are: Possibility of summarization over dynamically shifting view (sequence of rows called window), e.g. when we ...