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 ...

Are Data Lakes Fake News?

Uli Bethke Big Data, Data Warehouse

The problem with the data lake Are data lakes fake news? The quick answer is yes and in this post I will show you why. Before we get started make sure to download our checklist for a successful data lake implementation. The biggest problem I have with data lakes is that the term has been overloaded by vendors and analysts ...

About the author

Uli Bethke LinkedIn Profile

Uli has 18 years’ hands on experience as a consultant, architect, and manager in the data industry. He frequently speaks at conferences. Uli has architected and delivered data warehouses in Europe, North America, and South East Asia. He is a traveler between the worlds of traditional data warehousing and big data technologies.

Uli is a regular contributor to blogs and books, holds an Oracle ACE award, and chairs the the Hadoop User Group Ireland. He is also a co-founder and VP of the Irish chapter of DAMA, a non for profit global data management organization. He has co-founded the Irish Oracle Big Data User Group.

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 ...

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 ...