Apache Iceberg vs Delta Lake for a multi-engine lakehouse

Apache Iceberg vs Delta Lake for a multi-engine lakehouse

A public TESRAC example report comparing Apache Iceberg and Delta Lake for teams that need reliable table formats across Spark, Trino, Flink, and cloud object storage.

Public fallback brief

• Iceberg is usually the safer default when the lakehouse must support multiple engines with minimal vendor coupling.

• Delta Lake remains compelling when the team is standardized on Databricks or wants the strongest managed-platform integration.

• The highest-risk decision point is not feature count; it is how governance, catalog operations, compaction, and rollback workflows will be owned.

Caveats

• Benchmark claims depend heavily on file size, catalog choice, engine version, and write pattern.

• Migration cost can dominate format differences if the team already has production tables in one format.

Source quality and freshness

Best evaluated with official table-format docs, engine compatibility matrices, vendor release notes, and production workload tests.

Create a new report before making a final platform decision because table-format support changes quickly.

Example next steps

1. Inventory the engines that must read and write the same tables.

1. Prototype compaction, schema evolution, rollback, and catalog failure recovery.

1. Choose the format that reduces operational ownership for the actual team.