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 ...

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 ...