Architecting AWS-Snowflake Lakehouses with Apache Iceberg Integration Patterns

AWS Snowflake Lakehouse: 2 Practical Apache Iceberg Integration Patterns

AWS Community Builder Aki identifies a paradigm shift where Apache Iceberg separates physical data from query engines. Systems can now maintain data sovereignty on S3 while utilizing Snowflake for high-performance analytics. This architecture allows tools like Athena, Glue, and Snowflake to access the same datasets simultaneously.

Why This Matters

Before the rise of lakehouse architecture, data was typically locked into specific platforms like Amazon Redshift or Snowflake internal tables, creating silos and limiting tool flexibility. By adopting Apache Iceberg, technical teams can decouple storage from compute, reducing operational costs by eliminating the need for data movement and complex on-premises gateways for BI tools like Power BI.

Key Insights

• Pattern 1 (Glue Catalog Integration) enables a read-only architecture where AWS retains data sovereignty and Snowflake serves strictly as a query engine.
• Pattern 2 (Catalog-Linked Database) utilizes the Iceberg REST Catalog to allow Snowflake users to perform both read and SQL-based write operations directly on S3.
• Snowflake’s native Power BI connector removes the requirement for EC2-based data gateways, which are often necessary in Redshift-centered designs.
• The Medallion Architecture is optimized by placing the Gold semantic layer in Snowflake while keeping Bronze and Silver layers in S3-based Iceberg tables.
• Snowflake Cortex AI facilitates natural language interactions with S3 Iceberg tables, moving platforms from SQL-heavy workflows to conversational interfaces.

Working Examples

Configuring Snowflake External Volume for S3 access.

CREATE EXTERNAL VOLUME IF NOT EXISTS sample_iceberg_volume STORAGE_LOCATIONS = ((NAME = 'my-s3-location' STORAGE_PROVIDER = 'S3' STORAGE_BASE_URL = 's3://path/to/catalog/' STORAGE_AWS_ROLE_ARN = 'arn:aws:iam::123456789012:role/my-role' STORAGE_AWS_EXTERNAL_ID = 'my_external_id'));

Creating a Glue Iceberg REST Catalog Integration for read/write access.

CREATE OR REPLACE CATALOG INTEGRATION glue_rest_catalog_int CATALOG_SOURCE = ICEBERG_REST TABLE_FORMAT = ICEBERG CATALOG_NAMESPACE = 'default' REST_CONFIG = (CATALOG_URI = 'https://glue.region.amazonaws.com' CATALOG_API_TYPE = AWS_GLUE CATALOG_NAME = '123456789012') REST_AUTHENTICATION = (TYPE = SIGV4 SIGV4_IAM_ROLE = 'arn:aws:iam::123456789012:role/my-role' SIGV4_SIGNING_REGION = 'ap-northeast-1') ENABLED = TRUE;

Practical Applications

• Use case: AWS-led ETL pipelines where Snowflake provides read-only access for BI reporting. Pitfall: Centralizing governance on AWS while Snowflake users attempt unauthorized writes, leading to metadata desynchronization.
• Use case: BI/AI workflows where Snowflake serves as the primary interface for updating S3-resident data. Pitfall: Neglecting dual governance configurations on both AWS and Snowflake, which can expose security vulnerabilities in the data sovereignty layer.

References:

• https://dev.to/aws-builders/aws-snowflake-lakehouse-2-practical-apache-iceberg-integration-patterns-812