Snowflake Iceberg tables let you keep table data in the Apache Iceberg format and use Snowflake to query it, while compatible external engines can access some Snowflake-managed Iceberg tables through Horizon Catalog. That makes Snowflake one possible compute engine for open-format data—not merely a storage engine. Snowflake still supplies compute, catalog and platform services, and adopting Iceberg does not by itself guarantee lower costs, faster queries or effortless portability.
What changes when a Snowflake table uses Iceberg?
A Snowflake Iceberg table is a Snowflake table object whose underlying data is stored as Parquet files in the Apache Iceberg table format. Iceberg metadata and manifests describe the table’s files and snapshots. A catalog tracks the current metadata reference and updates it as the table changes; a compute engine reads that table state to run queries or other workloads.
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Those are distinct roles: storage holds the files, the catalog coordinates table state, and compute processes the data. Iceberg standardizes an important part of the boundary between them. It can make the same table usable by more than one compatible engine, reducing the need to create a separate proprietary copy for every system. But an open format does not make every engine compatible with every table feature, nor does it remove the need for catalog access, authentication, permissions and operational controls.
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Snowflake’s architectural history helps put this in context. Its 2016 SIGMOD architecture paper described storage, virtual warehouses for query execution and cloud services for management and metadata. That paper is a historical account, not a complete description of the current service. Iceberg extends the separation of data storage and compute choices; it does not turn Snowflake into storage alone.
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Which Iceberg arrangement fits your control and operations model?
There are two broad Iceberg management choices in Snowflake’s documentation. With Snowflake as catalog, Snowflake manages table metadata and lifecycle tasks such as compaction. With an external catalog, that system remains authoritative and Snowflake connects through a catalog integration; documented Snowflake platform support is more limited.
| Decision | Snowflake-managed Iceberg | Externally managed Iceberg | Native Snowflake table |
|---|---|---|---|
| Catalog authority | Snowflake | External Iceberg catalog, connected through a catalog integration | Snowflake; Iceberg catalog interoperability is not established for this table type |
| Where data can live | Snowflake storage or customer-managed cloud storage through an external volume | External cloud storage managed by the customer or provider | Snowflake-managed storage; the cited Iceberg documentation does not establish otherwise |
| Snowflake support and maintenance | Full Snowflake platform support is documented; Snowflake handles lifecycle maintenance such as compaction | More limited Snowflake platform support; responsibilities depend on the external catalog and storage setup | Not compared in the cited Iceberg documentation |
| Other compute engines | Horizon Catalog can provide compatible external engines access, subject to setup and supported capabilities | Depends on the external catalog, engine and supported Iceberg capabilities | Not established as an open-format interoperability path in the cited Iceberg documentation |
| Recovery responsibility | For tables in customer-managed external storage, Snowflake does not provide Fail-safe; the customer is responsible for data protection and recovery | Customer/operator must coordinate external storage, catalog protection and recovery | Not compared in the cited Iceberg documentation |
The table highlights different management boundaries, not a universal ranking. The cited materials do not establish a feature-by-feature comparison between native Snowflake tables and Iceberg tables, so confirm support for the specific operations, table features and recovery needs in your planned configuration.
Can other engines query Snowflake Iceberg tables?
Yes, through Horizon Catalog, when the engine, table capabilities and access configuration are compatible. Snowflake lists Apache Spark, Dremio, DuckDB, PyIceberg, StarRocks, Trino, Apache Doris and Apache Flink among external engines. The list is not exhaustive, and a name on the list is not a guarantee that every version, operation or table feature is supported.
Snowflake describes read and write access for Snowflake-managed Iceberg v2 and v3 tables. External access still requires the relevant catalog endpoint, authentication, permissions and access to the underlying storage. Check the exact engine integration and feature support before relying on cross-engine writes or less common Iceberg capabilities.
For externally cataloged tables, interoperability depends on the external catalog as well as the engines and Iceberg capabilities involved. Snowflake’s Open Data Sharing documentation also describes reading externally managed Iceberg tables and sharing Iceberg or Delta tables with Snowflake and non-Snowflake consumers. Those are product capabilities with their own access controls and setup—not evidence that all consumers can read any table without configuration.
What is the current Open Catalog path for new customers?
Snowflake’s current Open Catalog overview directs new customers to Horizon Catalog for Iceberg interoperability. It says customers who have never created an Open Catalog account cannot create their first one, while existing Open Catalog customers may continue and create additional accounts. The overview describes Open Catalog as a managed service for Apache Polaris built on the Iceberg REST protocol. Because availability and product names can change, confirm the current onboarding path with Snowflake before designing around it.
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Does Iceberg make Snowflake cheaper?
Not automatically. Snowflake says accounts incur virtual warehouse compute and cloud services charges when working with Iceberg tables. Snowflake storage is billed by Snowflake; with customer-managed external storage, the cloud provider bills storage directly. Depending on where data and compute sit, cross-cloud or cross-region access can add transfer charges. External-engine access through Horizon Catalog may also involve catalog API and storage request charges.
Snowflake documentation accessed in 2026 lists a Horizon Catalog rate of 0.5 credit per million API calls and says billing was scheduled to begin in the second half of 2026, subject to change. Treat both the rate and billing status as time-sensitive; verify the current terms for your account and configuration rather than assuming that the scheduled date or figure still applies.
Snowflake’s editions documentation says unit costs vary by edition, region and whether an account is On Demand or Capacity. Virtual warehouses are separate compute clusters, and resource monitors can track warehouse credit use. A useful comparison therefore accounts for more than storage price:
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- Compute in each engine, including the warehouse size, runtime and concurrency needed for representative work.
- Storage charges and any storage API or request charges in the chosen location.
- Data transfer between regions or clouds, and between storage and the engines that query it.
- Catalog or API charges, where applicable.
- Engineering time and operational work for permissions, compatibility, table maintenance and recovery.
Moving files to customer-managed storage can change who bills for storage, but it does not make storage free or eliminate Snowflake compute charges. Total cost depends on data location, query patterns, file layout, engine choice and the charges of every service involved.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Does Iceberg make queries faster?
The cited materials do not establish a universal performance win over native Snowflake tables or other query engines. Snowflake’s 2024 announcement promotes price-performance and describes Iceberg tables as providing “compute engine interoperability over a single copy of data.” Those are the vendor’s product claims and description, not independent benchmark results.
Measure your own workload with representative data and queries. Include cold and warm cache behavior where relevant, concurrency, file sizes and layout, metadata and manifest overhead, write patterns, compaction, and access across regions. Compare the actual charges for each engine and storage path as well as query time; otherwise a faster result may simply reflect a different configuration or a cost that has shifted elsewhere.
How should you decide whether to use Snowflake Iceberg?
Choose based on the workload and the management boundary you want, not on the word “open” alone. Before committing, answer these questions:
- Where is the data now? Identify whether it is in Snowflake storage or customer-managed cloud storage, and what transfers a query will require.
- Which engines must use it? Name the engines and operations, then confirm compatibility for the precise table type, Iceberg version and integration.
- How will the table be written and maintained? Account for write frequency, compaction and any external catalog coordination.
- Who owns governance and access? Assign responsibility for credentials, permissions, policies and the catalog’s current metadata reference.
- Who protects and recovers the data? In particular, do not assume Snowflake Fail-safe covers Iceberg tables in customer-managed external storage.
- What does a representative test cost? Measure compute, storage, transfer, catalog/API and request charges, plus operational effort, under realistic concurrency and data volumes.
Snowflake-managed Iceberg is the more direct fit when you want Snowflake to manage catalog and lifecycle tasks while retaining an open-format interoperability path for compatible engines. An external catalog is a different trade-off: it preserves that catalog’s authority but carries more coordination and compatibility responsibility. If a required feature, recovery obligation or measured workload does not fit either model, the existence of Iceberg support is not, by itself, a reason to migrate.
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