starburst galaxy, as a saas offering, just keeps slipping in nice bits of features & functionality — this one tackles hiding the underlying storage table of an iceberg materialized view
Tag Archives: trino
recap of the inaugural iceberg summit (my top 5 observations)
tl;dr – iceberg is pervasive, the real fight is for the catalog, concurrent transactional writes are a bitch, append-only tables still rule, and trino is widely adopted
joining spark dataframes with identical column names (an easier way)
presenting an easier solution to the problem of colliding column names when joining spark dataframes than i previously offered in my most popular post that just happens to be four years old — some things do age well
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?
trino: an origin story (nailed it!)
the full trino origin story complete with architectural walkthru and comparisons with other frameworks like hive & spark all in a single video? a single video that is < 20 minutes long? yep, and the creator nailed it!
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)
data universe 2024 workshops (feedback appreciated)
feel free to come and test drive my four trino/starburst workshops i will be delivering at data universe 2024
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!
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