You use Snowflake Iceberg tables primarily to leverage Snowflake's powerful query engine on data stored externally in your own cloud storage, especially for existing data lakes you don't want to migrate.
Key Reasons to Use Snowflake Iceberg
Snowflake Iceberg tables offer a compelling solution for organizations managing data outside of Snowflake's native storage but still wanting to utilize Snowflake's robust platform for analytics and processing.
As highlighted in the reference, Apache Iceberg™ tables for Snowflake are:
ideal for existing data lakes that you cannot, or choose not to, store in Snowflake.
This means you can connect Snowflake compute directly to data organized using the Iceberg format in your external cloud storage (like S3, ADLS Gen2, or GCS).
Here's a breakdown of the main advantages:
- Querying Existing Data Lakes: Access and query data residing in your current data lake infrastructure without the need for costly, time-consuming, or complex data migration into Snowflake's managed storage. This is particularly valuable for large datasets already established in external cloud storage.
- Performance and Query Semantics: Gain access to Snowflake's optimized query performance, familiar SQL interface, and advanced features (like joins, aggregations, window functions) directly on your external Iceberg data, similar to querying data within Snowflake itself.
- Data Governance and Management: Utilize Iceberg's open standard for managing large, slow-changing table datasets. This includes features like schema evolution, partition evolution, time travel, and snapshot isolation, which are managed externally but surfaced through Snowflake.
- Cost Efficiency: Avoid the storage costs associated with replicating large datasets into Snowflake's internal storage. You pay for Snowflake compute usage and storage costs in your own cloud provider account, offering flexibility in cost management.
- Flexibility: Maintain control over your data's physical location and management using tools outside of Snowflake, while still gaining the analytical power of the Snowflake platform.
- Open Format: Leverage the open Iceberg standard, which is supported by various engines and tools, ensuring interoperability with your broader data ecosystem.
Practical Benefits
- Connect to Data In Place: Ideal for scenarios where data is already in S3, ADLS Gen2, or GCS and needs to remain there for other workflows or cost reasons.
- Enable New Use Cases: Allows analytics teams using Snowflake to easily access data previously siloed in a data lake.
- Simplify Architecture: Reduces the need for complex ETL/ELT pipelines just to move data into Snowflake for analysis.
In essence, Snowflake Iceberg tables bridge the gap between Snowflake's powerful capabilities and your externally managed, large-scale data lakes formatted with the Iceberg standard, providing a performant and flexible solution for data access and analytics.
