Tag Archives: data_engineering

pystarburst in 90 seconds (try it)

still thinking about trying to get a pystarburst code stub up/n/running? starburst galaxy makes it pain free and you can even get your first dataframe created via python in under 90 seconds — why not give it a try?

hive to iceberg migration tool (rev1)

they had a need for an iceberg migration tool, I wrote an iceberg migration tool — i committed it as a github project, then i promoted a github project (i’ve got macklemore’s thrift shop in my head as i write this excerpt)

pystarburst via a jupyter notebook (exploring the tpc-h dataset)

ready to explore pystarburtst via a jupyter notebook? this post points you to a single-click solution to spin up jupyter that has sample notebooks ready to run — you’re welcome!

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

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