Unlocking the Power of Apache Iceberg with Snowflake

In today’s data-driven world, organizations are constantly looking for efficient ways to manage, store, and query large-scale datasets. Apache Iceberg has emerged as a powerful open-source table format designed to bring reliability, performance, and flexibility to data lakes. With Snowflake’s support for Apache Iceberg, organizations can now unlock even greater capabilities, seamlessly combining the best of both worlds: Snowflake’s cloud data platform and Iceberg’s open-table format.

In this article, we’ll explore what Apache Iceberg is, why it’s valuable, and how you can leverage it with Snowflake to maximize your data strategy.

What is Apache Iceberg?

Apache Iceberg is an open-source table format originally developed at Netflix to address common challenges in data lakes, such as performance bottlenecks, data consistency, and schema evolution. Unlike traditional Hive-based table formats, Iceberg provides a robust foundation for large-scale analytics by offering:

• ACID compliance for consistent and reliable data transactions
• Schema evolution without breaking existing queries
• Time travel and rollback capabilities
• Partitioning improvements that avoid performance pitfalls
• Compatibility with multiple engines, including Spark, Trino, Flink, and Snowflake

By adopting Iceberg, organizations can create a more efficient and flexible data lake architecture that supports modern analytical workloads.

Why Use Apache Iceberg with Snowflake?

Snowflake has long been a leader in cloud-based data warehousing and analytics, offering ease of use, scalability, and performance. With its recent support for Iceberg, Snowflake provides organizations with the ability to:

• Query Iceberg tables directly without ingesting data into Snowflake
• Optimize performance by leveraging Snowflake’s query engine on Iceberg datasets
• Retain data lake flexibility while benefiting from Snowflake’s governance and security features
• Reduce data duplication by eliminating the need for additional copies in Snowflake

This integration allows teams to maintain their existing Iceberg-based data lakes while still leveraging Snowflake’s advanced analytics and governance capabilities.

How to Use Apache Iceberg with Snowflake

1. Setting Up Apache Iceberg Tables in Snowflake

To start using Iceberg in Snowflake, you need to create and register your Iceberg tables. Snowflake currently supports querying external Iceberg tables stored in cloud object storage (AWS S3, Azure Blob Storage, or Google Cloud Storage).

Step 1: Create an External Volume

An external volume is required to access the object storage where Iceberg tables reside.

CREATE EXTERNAL VOLUME my_iceberg_volume 
STORAGE_LOCATIONS = (
  's3://my-data-lake/' 
);        

Step 2: Register an Iceberg Table in Snowflake

Once the external volume is set up, you can register your Iceberg table with Snowflake.

CREATE ICEBERG TABLE my_iceberg_table EXTERNAL_VOLUME = my_iceberg_volume LOCATION = 's3://my-data-lake/iceberg_table/';        

This step allows Snowflake to recognize and interact with your Iceberg tables.

2. Querying Iceberg Tables in Snowflake

Once your Iceberg table is registered, you can query it just like any other table in Snowflake.

SELECT * FROM my_iceberg_table WHERE event_type = 'purchase';        

Snowflake will execute the query efficiently, leveraging Iceberg’s metadata for optimized performance.

3. Writing Data to Iceberg Tables from Snowflake

Snowflake also allows users to write data back to Iceberg tables.

INSERT INTO my_iceberg_table SELECT * FROM my_snowflake_table WHERE event_date > '2024-01-01';        

This ensures that data remains synchronized across both Snowflake and the Iceberg table format.

Best Practices for Using Apache Iceberg with Snowflake

? Optimize Metadata Management

Iceberg maintains metadata files to track table snapshots and schema changes. Regularly optimizing metadata can improve query performance.

? Leverage Partitioning & Pruning

Iceberg’s hidden partitioning enables efficient query pruning without requiring users to specify partitions manually.

? Monitor Performance Metrics

Use Snowflake’s query profiling tools to analyze performance and adjust your storage configurations for optimal results.

? Governance & Security

Integrate Snowflake’s governance features, such as access control and auditing, to maintain data security while leveraging Iceberg’s open format.

Final Thoughts

The integration of Apache Iceberg with Snowflake represents a significant step forward in modern data lakehouse architectures. By combining Iceberg’s flexibility with Snowflake’s performance and governance capabilities, organizations can achieve a highly scalable, cost-efficient, and secure data platform.

Whether you're looking to modernize your data lake or optimize your analytics strategy, Snowflake + Iceberg offers a powerful solution for managing your data at scale.

Are you already using Apache Iceberg with Snowflake? Share your experiences and insights in the comments!