i had so much fun publishing my first pystarburst post and running it in starburst galaxy that i wanted to share some more examples – i ported my aviation dataset analytical queries to python and the dataframe api
Tag Archives: tutorial
hive acid transactions work on trino (can even update a partitioned column)
it seems that folks who haven’t used hive in production are always quick to say that hive doesn’t have classic crud operations, much less the merge statement, and that simply isn’t true – this post shows you that you can create a hive acid table and mutate its contents with trino
configuring the cache service (starburst enterprise)
showcasing a video walk-through of configuring and validating the caching service for starburst enterprise which enables table scan redirection, materialized views, and data products
pystarburst (the dataframe api)
the dataframe api is finally available for trino and starburst galaxy thanks to the pystarburst libraries — take a peek at some example usages in this quick validation run
building a sql-based data pipeline with trino & starburst (5 slick videos)
a collection of videos presented as an overview of how you could build a sql-based data transformation pipeline utilizing trino/starburst and automating it with dbt
better iceberg materialized views in galaxy (no staleness check)
i’m happy to report that some code changes were made since my last post on materialized views in starburst galaxy and the (mostly useless) “staleness check” is not being executed any more
determining # of splits w/trino/starburst/galaxy (iceberg table format)
a prior post tackled this same quest of understing how trino decides how many splits to use in a query with the hive table format — it ended with a question of how iceberg tackles the same problem which is answered in this post
delta lake in starburst galaxy (intro & integration)
delta lake is a popular data lake table format and the trino engine, and starburst galaxy, easily integrate with it all while using your favorite cloud provider’s object store thanks to galaxy’s great lakes connectivity
determining # of splits w/trino/starburst/galaxy (hive table format)
ever wondered how trino decides how many splits to use in a query when reading files from your data lake — if so, come along and ride on a fantastic voyage
starburst galaxy’s materialized views (using apache iceberg)
join me on a quick test drive of the features of materialized views in starburst galaxy (saas offering powered by trino) which use apache iceberg for persistence and features some pretty cool features around snapshots and awareness of stale data