Iceberg vs Delta Lake vs Hudi vs Paimon
Which open table format should sit at the table layer?
Iceberg has the widest multi-engine support, Delta is strongest inside the Databricks ecosystem, Hudi is built around upserts and incremental reads, and Paimon targets streaming-first workloads.
| Apache Iceberg | Delta Lake | Apache Hudi | Apache Paimon | |
|---|---|---|---|---|
| Primary strength | Multi-engine neutrality | Databricks integration | Upserts and incremental pulls | Streaming ingestion |
| Engine breadth | Widest | Broad and growing | Moderate | Narrower, Flink-centric |
| Update strategy | Copy-on-write and merge-on-read | Copy-on-write and deletion vectors | Copy-on-write and merge-on-read | Merge-on-read, LSM structured |
| Partitioning | Hidden partitioning, evolvable | Directory based | Directory based | Directory based |
| Schema evolution | Full, by field ID | Full | Full | Full |
| Catalog requirement | Required, several options | Optional, path based works | Optional | Required in practice |
| Governance model | Delegated to the catalog | Unity Catalog when managed | Delegated | Delegated |
Choosing
- You expect several engines to read and write the same tables Iceberg, for the breadth of independent implementations
- Your platform is centred on Databricks Delta, since the integration advantages are real
- Your workload is dominated by upserts and incremental consumption Hudi, which was designed around exactly that
- You are building streaming-first pipelines on Flink Paimon, for its LSM-oriented design
Caveats
- Apache XTable and Delta UniForm both let one physical dataset present as more than one format, which makes this choice less permanent than it once was.
- Engine support changes quickly. Verify the specific combination you plan to run, especially for concurrent writes, rather than trusting a general compatibility claim.