Delta Lake vs Iceberg vs Hudi: Picking a Format You Can Live With

Apache Iceberg, Delta Lake, and Apache Hudi have largely converged on core lakehouse capabilities such as ACID transactions, schema evolution, and time travel, making workload characteristics, catalog interoperability, and migration costs the main decision factors. Hudi is positioned for high-frequency change data capture and upsert-heavy workloads because its record-level indexing and incremental processing can reduce write costs, although cited benchmark results come from Hudi maintainers and use uneven software versions. Iceberg offers the broadest support across query engines, cloud warehouses, and catalogs, making it suited to organizations prioritizing multi-engine and multi-cloud flexibility, while Delta Lake remains a strong choice for teams centered on Databricks and Spark, particularly for mature merge and streaming workflows. Iceberg v3 adds features including deletion vectors, row lineage, semi-structured data support, and enhanced timestamps, but may limit compatibility with engines that have not yet adopted the version. Moving among formats generally requires substantial engineering work to rewrite jobs, validate results, retrain teams, and operate parallel systems, so organizations are encouraged to assess their actual write and read patterns, preserve catalog portability, and consider format-agnostic platform layers that allow multiple table formats to coexist.

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