Build Secure Data Mesh with AWS and Partner Solutions

This article explains how to build secure data mesh architectures using AWS and partner solutions like Databricks and Snowflake, focusing on financial services implementations.

• Data mesh shifts from centralized platforms to domain-oriented, distributed data ownership models
• Apache Iceberg enables cross-platform compatibility across AWS, Databricks, and Snowflake query engines
• Three critical requirements: Cross-Catalog Metadata Federation, Cross-Account Authentication/Authorization, Distributed Policy Enforcement
• AWS Lake Formation provides fine-grained access control and credential vending for secure data sharing
• AWS can function as both data producer (sharing via Glue Data Catalog) and consumer (accessing partner platforms)
• Databricks uses GDC API for metadata; requires IAM roles for S3 access; needs custom policy synchronization
• Snowflake uses Iceberg REST endpoints; supports Lake Formation credential vending; requires policy replication
• Organizations must establish standardized policy definitions translatable across all platforms for consistent governance

Successful data mesh implementation requires balancing domain autonomy with enterprise-wide governance through open table formats, consistent identity management, and synchronized access policies across heterogeneous platforms.