Apache Paimon alternatives

3 tools to consider instead of Apache Paimon, shown against it.

Apache Paimon Apache Hudi Apache Iceberg Delta Lake
Vendor Apache Software Foundation Apache Software Foundation (originated by Uber) Apache Software Foundation Linux Foundation (originated by Databricks)
Pricing model Open source + paid options Open source + paid options Open source + paid options Open source + paid options
Free tier Yes Yes Yes Yes
Deployment Self-hosted Self-hosted Self-hosted Self-hosted
Open source Yes (Apache-2.0) Yes (Apache-2.0) Yes (Apache-2.0) Yes (Apache-2.0)
Best for Streaming-heavy pipelines needing fast continuous writes and efficient batch analytics on one table. Pipelines with heavy upsert/delete volume, such as CDC ingestion or GDPR-driven record deletion. Teams building a multi-engine lakehouse who need one open table format multiple compute engines can share. Spark-centric teams wanting reliable, versioned tables on a data lake without a separate warehouse.
Pricing

Free, open-source table format with no vendor pricing; costs are limited to the compute and storage it runs on.

Pricing has not been verified yet — see the vendor's site.

Free, open-source table format; no vendor pricing, though managed services built on Hudi (such as Onehouse) are sold separately.

Pricing has not been verified yet — see the vendor's site.

Free, open-source specification and libraries; no vendor pricing, though managed catalog and compute services built on Iceberg are sold separately by cloud vendors.

Pricing has not been verified yet — see the vendor's site.

Free, open-source table format; no vendor pricing of its own, though Databricks and other lakehouse platforms charge separately for the compute that reads and writes Delta tables.

Pricing has not been verified yet — see the vendor's site.

Features
  • LSM-tree storage layout for fast streaming upserts
  • Unified streaming and batch reads on the same table
  • Deep native integration with Apache Flink
  • Background compaction to keep batch queries efficient
  • Schema evolution and time travel
  • Changelog production for downstream streaming consumers
  • Interoperability bridges toward Iceberg and Hive metastore
  • Record-level upserts and deletes optimized for CDC pipelines
  • Copy-on-Write and Merge-on-Read storage layouts
  • Built-in indexing for fast upsert/delete lookups
  • Incremental queries returning only changed records
  • Automatic file sizing, clustering, and compaction services
  • Multi-engine support (Spark, Flink, Presto, Trino, Hive)
  • Time travel and savepoints for rollback
  • ACID transactions with snapshot isolation on data-lake files
  • Time travel and rollback to previous table snapshots
  • In-place schema evolution (add, drop, rename, reorder columns)
  • Hidden partitioning with partition evolution without rewriting data
  • Engine-agnostic: readable/writable by Spark, Trino, Flink, and more
  • Manifest-based metadata avoiding costly file-listing operations
  • Support for Parquet, ORC, and Avro file formats
  • ACID transactions via an ordered transaction log
  • Time travel to query or roll back to prior table versions
  • Schema enforcement and controlled schema evolution
  • Concurrent batch and streaming reads/writes to the same table
  • Data compaction and Z-ordering for query performance
  • Change Data Feed for incremental downstream processing
  • Native support in Apache Spark, with connectors for Trino, Flink, and Presto

In the index now