Build a Multi-Engine Stack on Snowflake Storage for Iceberg & Horizon Catalog

Data engineers running multi-engine stacks pay two hidden taxes. The first is complexity: duplicate pipelines feeding the same tables, governance policies that drift between platforms, and two places to debug when something breaks. The second is bucket storage infrastructure: bucket permissions, lifecycle rules, compaction schedules, and the ever-present risk that one misconfiguration takes a table down permanently. As AI workloads demand more connected, higher-quality data, both taxes are becoming impossible to ignore.

This lab shows you how to eliminate both. Using Apache Iceberg as a shared open table format, Snowflake Storage for Iceberg tables (currently in public preview) handles the infrastructure so your engineers don't have to — one CREATE ICEBERG TABLE statement and Snowflake manages the maintenance, the compaction and the lifecycle policies. Horizon Catalog governs your tables, so Snowflake and Databricks both access the same governed data. And with Iceberg v3, your tables handle semi-structured data natively — no schema workarounds required. All the interoperability. None of the complexity. None of the lock-in.


In this lab, you'll:

• Create Iceberg tables on Snowflake Storage (public preview) with a single SQL statement — no bucket configuration, lifecycle policies, or compaction schedules required.
  
• Register and govern your tables in Horizon Catalog so every engine reading your data sees the same schema and the same access controls.
  
• Configure multi-engine reads so Databricks accesses your Snowflake-stored Iceberg tables directly — no data copies, no additional ETL.
  
• Ingest and query semi-structured data using Iceberg V3's native VARIANT support — complex nested types without schema workarounds.
  
• Leave with a working architecture template ready to adapt for your own multi-engine environment.