Why is concurrency overrated to measure performance of data warehouse platforms?

Uli Bethke Redshift

The difference between making a good and a bad decisions often comes down to the quality of the pre-defined metrics. If the metric is poor so will be the decision. When comparing performance between different technologies such as Google Big Query (based on a distributed file system - Colossus to be precise) and MPP technologies such as Redshift, people tend ...

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.

Comparing Window Function Features by Database Vendors

Jiří Mauritz Data Warehouse, Redshift, SQL for Analysis, Window Functions

We will round off the series on window functions with comparison of what database vendors offer. There are various mutations of window functions and every vendor supports a different subset or feature. Some also add extra window functions or features beyond standard ANSI SQL. One of the most powerful features is user-defined aggregate functions (UDAF), which some databases allow using ...

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 ...

Query Offload with Redshift Spectrum. Use Cases and Limitations

Jiří Mauritz Data Warehouse, Redshift

Update: An earlier version of this article claimed that Spectrum runs Presto under the hood. This is incorrect. Spectrum uses its own query layer. Query offload from relational data warehouses to cheaper distributed storage seems to be all the rage these days. In this blog post we examine what works and what the limitations are. What is Query Offload? Let’s ...

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 ...