Access Amazon S3 Iceberg Tables in Databricks Using AWS Glue and SageMaker Lakehouse

Introduction to SageMaker Lakehouse

Amazon SageMaker Lakehouse delivers a unified, open, and secure platform that integrates seamlessly with existing data infrastructure. It combines the cost-effectiveness of S3 storage with robust data lake capabilities, while maintaining warehouse-grade performance and reliability.

Key Features and Capabilities

  • Open source Apache Iceberg REST APIs for universal data access
  • Compatibility with multiple AWS services including Redshift, EMR, and Athena
  • Fine-grained access controls through AWS Lake Formation
  • Unified policy administration for data lakes in Amazon S3

Technical Implementation Steps

  • Configure Lake Formation prerequisites and IAM roles
  • Register S3 locations as data lake locations
  • Set up appropriate database and table permissions
  • Create and configure Databricks workspace with specific runtime settings
  • Implement Spark configurations for Iceberg integration

Security and Access Control

The solution leverages AWS Lake Formation’s robust security framework to:

  • Enable fine-grained access controls on data
  • Manage credential vending for secure data access
  • Implement role-based permissions at database and table levels

Integration Benefits

This architecture enables organizations to:

  • Maintain a single source of truth for data
  • Streamline policy administration
  • Enable secure cross-platform data access
  • Optimize cost with efficient storage utilization

Click here to learn more about accessing Amazon S3 Iceberg tables from Databricks using AWS Glue