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Snowflake’s Iceberg support lets its compute and data platform work with tables stored in the Apache Iceberg open table format, including data in customer-managed cloud storage. Compatible external engines can also access some Snowflake-managed Iceberg tables through Horizon Catalog. That makes table storage and compute less tightly coupled—but Snowflake is not merely a storage engine: it still supplies compute, catalog options, governance and other platform services. Whether this design is cheaper or faster depends on your workload and operating costs.
What changes when a Snowflake table uses Iceberg?
The main change is the table format and the interoperability boundary. Snowflake Iceberg tables use Parquet data files, while Iceberg metadata and manifests describe the files in a table snapshot. A catalog tracks the current metadata reference and updates it as the table changes. The catalog and the engine that runs queries are distinct roles.
That separation can let compatible engines work with the same open-format table rather than requiring every system to maintain its own proprietary copy. But it does not make every table universally accessible: engines must support the relevant Iceberg capabilities, and access still depends on the catalog, credentials, permissions and storage configuration.
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The three roles in the architecture
- Storage and data files: Parquet files live in Snowflake storage or, for supported configurations, customer-managed cloud storage.
- Iceberg metadata and catalog: Metadata describes the table’s files and snapshots; the catalog maintains the current metadata pointer.
- Compute engines: Snowflake or a compatible external engine reads and processes the table, subject to supported features and configured access.
This is the precise version of the “warehouse as storage engine” idea: separate the table’s format and location from the choice of compute. Snowflake remains a data platform and query engine, not just a place to park files. Snowflake’s 2016 architecture paper describes an earlier multi-cluster, shared-data design with storage, virtual warehouses and cloud services; it is useful historical context, not a complete description of today’s implementation.
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Which Iceberg management model fits?
The practical choice is whether Snowflake controls the Iceberg catalog or an external catalog remains authoritative. Snowflake-managed Iceberg tables can use Snowflake storage or customer-managed external storage accessed through an external volume. For externally cataloged tables, Snowflake connects to the external catalog through a catalog integration.
| Decision | Snowflake-managed Iceberg | Externally managed Iceberg |
|---|---|---|
| Catalog authority | Snowflake is the catalog. | An external Iceberg catalog is authoritative; Snowflake connects through a catalog integration. |
| Storage | Snowflake storage or supported customer-managed external cloud storage. | External cloud storage managed by the customer or its provider. |
| Snowflake support and maintenance | Snowflake documents full platform support and lifecycle maintenance, including compaction. | Snowflake documents more limited platform support; catalog and storage coordination remain external responsibilities. |
| Access from other engines | Horizon Catalog can expose Snowflake-managed Iceberg tables to compatible external engines. | Interoperability depends on the external catalog, engine and Iceberg capabilities in use. |
| Protection and recovery | Responsibilities depend on the storage configuration and applicable Snowflake features. | For tables in customer-managed external storage, Snowflake says it does not provide Fail-safe; the customer is responsible for data protection and recovery. |
Native Snowflake tables are another option, but the cited Snowflake materials do not establish a universal feature, price or performance comparison between native tables and Iceberg. Treat the format choice as an architecture decision to validate against your required integrations, table features and operating model—not as an automatic upgrade.
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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 supported external-engine integrations. The list is not exhaustive, and listed integrations do not imply that every engine supports every table feature.
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Snowflake’s Open Catalog overview says new customers should use Horizon Catalog for Iceberg interoperability. Customers who have never created an Open Catalog account cannot create their first one; existing Open Catalog customers may continue and create additional accounts. Snowflake describes Open Catalog as a managed service for Apache Polaris built on the Iceberg REST protocol. Because product availability and integration support can change, check the current Snowflake documentation before choosing a catalog path.
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. Sharing is a platform feature with its own access controls; it is not proof that every consumer can read every table without configuration.
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What costs remain when storage is external?
External storage changes who bills for storage; it does not make the workload free. Snowflake states in its documentation: “Snowflake bills your account for virtual warehouse (compute) usage and cloud services when you work with Iceberg tables.” If you use Snowflake storage, Snowflake storage charges apply. If you use customer-managed external storage, the cloud provider bills storage directly.
- Compute and cloud services: Snowflake bills these for Iceberg work, including queries and other operations.
- Storage: The storage provider bills customer-managed storage; Snowflake bills Snowflake storage.
- Transfer: Cross-cloud or cross-region access can generate data-transfer charges.
- Requests and catalog access: Depending on configuration, Horizon Catalog access may add catalog API and storage-request charges.
- Operations: Maintenance, monitoring, access setup, recovery and engineering time also affect the total cost, even when they are not billed as a single line item.
Snowflake documentation accessed in 2026 lists a Horizon Catalog rate of 0.5 credit per million API calls and says feature billing was scheduled to begin in the second half of 2026, subject to change. Verify the current rate and billing status before forecasting spend. Snowflake also says unit costs vary by edition, region and whether an account is On Demand or Capacity; its resource monitors can track virtual-warehouse credit use.
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Build the comparison around the whole workload: compute, storage, transfer, catalog and storage requests, maintenance and engineering operations. External storage may be a better fit when it aligns with an existing data lake or other engines, but Iceberg alone does not establish savings. Data location, query patterns, concurrency, file layout and the charges of each service determine the result.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Does Iceberg make Snowflake faster?
There is no universal speed conclusion established by the cited material. Snowflake’s 2024 announcement presents Iceberg as a way for its elastic compute engine to access external open-format data, and promotes price-performance and avoiding copied data. Those are the vendor’s stated product goals, not independent benchmark findings. The available material does not establish that Iceberg is always faster or cheaper than native Snowflake tables, or that Snowflake outperforms Spark, DuckDB, Trino or another engine on a defined workload.
Test the systems and table formats you are actually considering. Use representative queries and writes, then record both elapsed time and the relevant cost meters. Include:
- Cold- and warm-cache behavior where it matters, plus expected concurrency.
- File sizes, layout, metadata and manifest overhead, and the effect of maintenance or compaction.
- Read and write workloads, not just a favorable query example.
- Data location and any cross-region or cross-cloud access.
- Required table features, governance controls and the recovery process.
How to decide whether Iceberg is right for your workload
- Map where the data already lives. Identify the storage provider, region and any transfer charges that would apply to Snowflake or external engines.
- Name the engines that must use the same table. Check each engine’s current integration, Iceberg version and required table-feature support.
- Choose the catalog owner. Decide whether Snowflake-managed lifecycle support or an external catalog’s authority better matches your operating model.
- Specify writes and maintenance. Document which systems write to the table, how concurrent changes are handled, and who manages file layout and compaction.
- Assign governance and recovery. Confirm permissions, authentication, protection and restoration responsibilities—especially when storage is customer-managed.
- Measure representative workloads. Compare query and write behavior, concurrency, transfers and all relevant billing meters before committing broadly.
Iceberg is most compelling when open-format tables and multiple compute choices solve a real data-access or duplication problem. If a workload depends on Snowflake-specific support, has no meaningful need for another engine, or gains no measurable operational or economic benefit, the extra catalog, access and recovery responsibilities may not be worthwhile.
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