Evaluating Apache Iceberg for Interoperable Lakehouse Architectures in Azure-Based Databricks and Snowflake Environments

This whitepaper evaluates Apache Iceberg as an interoperable open table format within Azure-based lakehouse architectures that combine Databricks for data engineering and Snowflake for analytical workloads. Many modern enterprise data platforms operate in hybrid environments where multiple compute engines must access shared datasets stored in cloud object storage. While Delta Lake provides strong performance and transactional guarantees within Databricks, cross-platform interoperability can introduce challenges related to data duplication, metadata fragmentation, governance alignment, and operational complexity. This paper analyzes Apache Iceberg as a potential interoperability layer capable of enabling multi-engine access to shared datasets while maintaining transactional consistency. The evaluation focuses on architectural design principles, metadata management models, cross-engine interoperability patterns, operational lifecycle considerations, and cost implications within Azure environments. The study provides a structured comparison between Delta Lake and Apache Iceberg, examines implementation strategies for enabling shared table access between Databricks and Snowflake, and outlines operational best practices for production deployments. The objective of this paper is not to position Apache Iceberg as a replacement for Delta Lake, but rather to assess its role as a strategic interoperability layer in multi-engine lakehouse architectures. This research aims to support data platform architects, data engineers, and enterprise technology leaders in making informed decisions about adopting open table formats to build scalable, interoperable, and future-ready data platforms.