Unlock your Data Potential with Snowflake Iceberg Tables

The Snowflake Data Cloud continues to stand out as a pioneer. Snowflake consistently introduces innovative features to simplify and optimize data storage and compute workloads. One such feature recently added by Snowflake is the support for the Iceberg table format, which is currently in public preview for all Snowflake customers.

In this article, we will discuss the architecture of Snowflake Iceberg tables, and how they perform compared to native and external Snowflake tables. Finally, we will explore different use cases where Iceberg tables are the ideal solutions and discuss some limitations.

Snowflake Iceberg Tables: A New Frontier

Iceberg tables in Snowflake represent a groundbreaking shift in how data can be managed and accessed. Unlike traditional Snowflake tables, Iceberg tables store data outside of Snowflake, leveraging public cloud object storage locations like Amazon S3, Google Cloud Storage, or Azure Storage. This data is stored in the Apache Iceberg table format, allowing Snowflake to access it using new objects called external volume and catalog integration.

The Architecture of Iceberg Tables

The architecture of Snowflake Iceberg tables is built on the Apache Iceberg open table format specification, which provides an abstraction layer over data files stored in open formats. This format supports several advanced features:

• ACID Transactions: Ensuring atomicity, consistency, isolation, and durability in all data operations.
• Schema Evolution: Allowing seamless updates and changes to the data schema over time.
• Hidden Partitioning: Automatically managing data partitioning to optimize performance.
• Table Snapshots: Enabling the capture and management of table states at different points in time.

Performance and Query Semantics

Snowflake Iceberg tables combine the performance and query semantics of regular Snowflake tables with the flexibility of external cloud storage. This combination makes them ideal for organizations with existing data lakes that either cannot or choose not to migrate all their data into Snowflake. By supporting the Apache Parquet file format, Snowflake ensures that Iceberg tables deliver robust performance for a wide range of data queries and workloads.

Use Cases and Limitations

Use Cases:

• Hybrid Data Architectures: Perfect for organizations utilizing a mix of on-premises and cloud storage.
• Data Lakes: Ideal for companies with large data lakes stored in public cloud object storage.
• Cost Optimization: Beneficial for optimizing storage costs by keeping infrequently accessed data outside of Snowflake.

Limitations:

• Current Version Constraints: As Iceberg tables are in public preview, there might be limitations in features and performance compared to fully native Snowflake tables.
• External Dependencies: Reliance on external storage services may introduce additional complexity in managing data access and security.

Conclusion

Snowflake's support for Apache Iceberg tables represents a significant advancement in data management and governance. By blending the power of Snowflake's query engine with the flexibility of external cloud storage, you can unlock new potential in their data architectures. As the feature evolves, we can expect even more robust capabilities and broader adoption across the industry.

You can read my article on medium:

https://medium.com/@ibbyrahmani/unlocking-you-data-potential-the-power-of-snowflake-iceberg-tables-e4c39b4fe5e8

#snowflake #snowflakedatacloud #snowflakeiceberg #datawarehouse #datacloud

Rohit Singh Saqib Mustafa

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