building trino data pipelines (with sql or python)

trino is well-known as a fast query engine, but it is also a robust transformation processing engine that allows data engineers to developer in sql and/or python

yarp: yet another rag post (this time using sql)

you don’t have to know python or bother your data scientists to start exploring genai concepts like rag; you just need a tool that offers these features in a familiar sql interface

optionality and common sense (why i returned to starburst)

i’m so excited to have returned to starburst and be focused on rebooting the devrel function, not to mention staying active in the trino and iceberg communities — long live the icehouse

iceberg acid transactions with partitions (a behind the scenes perspective)

a port of my prior post taking a deeper look at what happens under the hood of hive with “acid” transactions — this time on iceberg tables with parquet files

well designed partitions aid iceberg compaction (call them ice cubes)

despite what you may have heard, partitions are not dead (yes, there are multiple tools in the shed) and using a well-defined partitioning strategy with apache iceberg can help prevent concurrency issues when compacting files

iceberg materialized views in galaxy (no más storage_schema)

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