i came to starburst just over two years ago for the framework’s focus on optionality; i’ve stayed for the opportunities that have been presented to me (i’m proud to be an all-star and glad to talk about open opportunities with you)
Author Archives: lestermartin
building scalar udf’s w/sql for trino (aka sql routines)
check out this quick set of simple examples showing how easily you can create sql-based user-defined functions (udf), formally referred to as trino sql routines, to allow more succinct queries and offer reusability
apache iceberg table maintenance (is_current_ancestor part deux)
as a follow-on to my earlier post about iceberg versioning (and the is_current_ancestor flag), i thought it would be useful to show working examples of the maintenance activities that are needed to manage the sprawl of data lake files that come with more and more versions
becoming a data engineer (yet another top 10 list)
after a recent class i was asked what skills someone needs to become a data engineer – there are plenty of these lists all over the internet, yet here i go assuming i know enough to jot down yet another; at least i put mine all in a single picture 😉
iceberg snapshot is_current_ancestor flag (what does it tell us)
i’ve noticed the is_current_ancestor column of the apache iceberg $history metadata table for a while now – it wasn’t until I got a direct question about it that i realized it was time to find out for sure
dbt cloud & starburst galaxy workshop (beta testers welcome)
interested in building a data pipeline with dbt cloud and starburst galaxy? if so, then this post presents recorded videos of 7 lab exercises plus the lab guide itself so you work through them on your own & at your pace
z-order (visualized)
when asked to compare sort-by with z-order for data lake tables i realized i finally needed to have a better understanding of what z-order is all about and my goal with this blog post is to present a simplified visualization of what’s going on and how it can help
ibis & trino (dataframe api part deux)
this is a port of the dataframe api code from my original pystarburst posting – this time i implemented the same scenarios with ibis, the portable python dataframe library, and had a blast doing it
viewing astronauts thru windows (more pystarburst examples)
i’ve got a fever and the only prescription is more pystarburst examples — this third installment is all about window functions via the dataframe api and like before, I present sql first for comparison
sql window functions explained (transparently as possible)
sql window functions aren’t really that complicated, but like everything else they deserve a decent introduction – i hope mine helps for anyone trying to wrap their head around what they are