Apache Hudi vs Apache Paimon
Apache Hudi
hudi.apache.org
Apache Paimon
paimon.apache.orgSide by side
| Figure | Apache Hudi | Apache Paimon |
|---|---|---|
| Price from | Free | Free |
| Free plan | Yes | Yes |
| Free trial | — | — |
| Open source | — | — |
| Windows | — | Yes |
| macOS | — | Yes |
| Linux | Yes | Yes |
| API | — | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 1 | 1 |
| Spec score · Data Version Control Tools | 8.1 (#12) | 9.0 (#2) |
| Security model | Hudi relies on the security posture of its underlying compute engine and storage environment. | The project says trust and authorization boundaries are generally enforced by the surrounding catalog, engine, service, operator configuration, and storage authorization. |
| Support | The site points users to community channels including Slack, GitHub, and the Hudi mailing list for community participation and technical help. | The project directs users to its user mailing list and GitHub issue tracker for help and issue reporting. |
| Vulnerability reporting | The security page asks people to report potential vulnerabilities privately to the Apache Security Team before public disclosure. | The project directs users to report possible vulnerabilities privately to [email protected] and not disclose them publicly before the project responds. |
Plans
In their own words
Apache Hudi
Apache Hudi is a free, open-source data lakehouse platform that adds database capabilities to data lakes and supports incremental processing for low-latency analytics. It can update and delete records, with pluggable indexing for change-data-capture and streaming workloads. Transaction features include atomic writes, snapshot isolation, and non-blocking concurrency controls. Users can query historical table versions, roll back changes, and review commit history. Automated table services handle clustering, compaction, cleaning, file sizing, and indexing, while schema evolution and enforcement accommodate changing data. Listed integrations include Apache Spark, Apache Flink, Kafka, Debezium, Amazon S3, Google Cloud Storage, Azure Blob Storage, Trino, and dbt. Some integrations are read-only, so Spark or Flink may write Hudi tables for those systems to query. Downloads include source releases and Maven bundles, with PGP verification advised. Hudi relies on the security posture of its compute and storage environment.
Apache Paimon
Apache Paimon is a lake format for realtime lakehouse architectures, supporting streaming and batch operations with Flink and Spark. Primary-key tables handle large-scale streaming updates through configurable merge engines, including deduplication, partial updates, aggregation, and first-row updates. Append tables support batch and streaming processing, small-file merging, and compaction with Z-order sorting. Paimon also provides ACID transactions, time travel, schema evolution, and metadata for petabyte-scale datasets and many partitions. Listed integrations include Flink, Spark, Hive, Trino, Presto, StarRocks, and Doris, though supported operations vary by engine and version. Documented CDC sources include MySQL, PostgreSQL, Kafka, MongoDB, and Pulsar. Other capabilities include blob and vector storage, full-text search, and global indexing. PyPaimon provides catalog, table, Arrow, and pandas APIs plus a command-line tool; core reads and writes do not require a JVM or a running Flink or Spark cluster. It is free at free, with users choosing connectors and storage plugins for their environment.