Open Data Infrastructure
How to Migrate from Snowflake to an Open Iceberg Lakehouse
A phased Snowflake-to-Iceberg migration guide covering coexistence, table design, catalogs, governance parity, validation, and cutover risk.
The worst Snowflake-to-Iceberg migration plan is the heroic one. It starts with a giant export, promises a clean cutover, and discovers the hard parts after the business is already nervous.
Start with coexistence, not a cliff
A serious migration starts by separating storage, table semantics, catalog control, query behavior, and governance. Snowflake can participate in Iceberg architectures, and Snowflake documents support for Apache Iceberg tables. That means the migration question is not "Snowflake or Iceberg?" The better question is which system owns which contract during each phase.
Start with coexistence. Keep critical workloads stable. Publish a narrow set of Iceberg tables where openness matters most. Validate readers, writers, policies, lineage, and cost before moving the next domain.
Use four phases
- Inventory: list tables, workloads, owners, data sharing paths, SQL dependencies, freshness needs, and governance controls.
- Design: choose table format conventions, catalog pattern, partition strategy, write path, and access model.
- Parallel run: write or replicate a small number of important tables into Iceberg and compare results.
- Cutover: move workloads only after query correctness, performance, lineage, access, and rollback are tested.
The hard part is not making files appear in object storage. The hard part is preserving trust while ownership moves.
The catalog decision is the migration decision
Iceberg tables need a catalog. That catalog is where table identifiers, metadata locations, and operations are coordinated. If you pick the catalog as an afterthought, you move the lock-in instead of reducing it.
For an ODI migration, prefer a catalog strategy that can support multiple engines, expose clear permissions, and keep table operations understandable. The Iceberg REST Catalog spec matters here because it defines an API boundary instead of tying every engine to one private integration.
Core idea: the goal is not to escape one vendor logo. The goal is to make table control portable enough that future compute choices are boring.
The pitfalls show up in the middle
Watch for platform-specific SQL, hidden data quality assumptions, identity mismatches, cost surprises, and dashboards that depend on warehouse behavior nobody documented. Also watch for governance gaps. If access rules get weaker during migration, you are not modernizing. You are creating a compliance incident with better file formats.
A good migration ends with fewer private assumptions than it started with. Anything else is just a relocation project.
Sources to start with
These are the primary sources I would start from when checking the claims in this piece.