Apache Iceberg vs Delta Lake: What are the differences?

In the rapidly evolving world of data lakehouses, Apache Iceberg and Delta Lake have emerged as two leading table formats, each offering unique advantages and converging features that blur their distinctions. Originally developed by Netflix and Databricks, respectively, both formats prioritize efficient data management capabilities such as ACID compliance, schema evolution, and time travel queries. Although once distinct, the competition between Iceberg and Delta Lake has driven convergence, leading to similar feature sets. However, their underlying mechanisms for metadata management differ; Iceberg uses manifest files while Delta Lake utilizes a Delta Log. The choice between these technologies often depends on the existing data ecosystem and specific organizational needs, with Iceberg being favored for its openness and compatibility with various compute engines, while Delta Lake offers deep integration with Databricks and Spark. As the industry moves towards a more unified data stack, Apache Iceberg's long-standing commitment to openness and its compatibility with Trino and other engines make it a compelling choice for organizations seeking flexibility and scalability in their data architectures.