Apache Paimon

7.4easy start · #6 of 36
in Data Version Control Tools

Free to practise onyes
Free trialnot on record
Well documentedyes
Runs where you workyes

Runs on api, Linux, Mac, self-hosted, Windows.

Apache Paimon is ranked #6 of 36 in data version control tools on The Geeks Club. It runs on API, Linux, macOS, Self-hosted, Windows. There is a free plan.

Apache Paimon plans and pricing

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Apache Paimon Free Open-source data lake platform; choose connectors and storage plugins for your environment paimon.apache.org · 4 Oct 2026

Compared on data version control tools

Free plan: Yes
Data scope: tables
Dataset branching: Yes
Point-in-time rollback: Yes
Snapshot granularity: table
Storage backend: bring_your_own
Deployment model: self_hosted

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The record

Purpose: Apache Paimon is a lake format for building realtime lakehouse architectures with streaming and batch operations using Flink and Spark.
Realtime updates: Primary key tables support large-scale updates and configurable merge engines, including deduplication, partial updates, aggregation, and first-row updates.
Append processing: Append tables support large-scale batch and streaming processing, automatic small-file merging, and data compaction with Z-order sorting.
Data management: Paimon supports ACID transactions, time travel, schema evolution, and metadata for petabyte-scale datasets and many partitions.
Compute integrations: The ecosystem compatibility page lists integrations for Flink, Spark, Hive, Trino, Presto, StarRocks, and Doris, with supported operations varying by engine.
CDC ingestion: The project homepage lists CDC pipelines for MySQL, PostgreSQL, Kafka, MongoDB, and Pulsar.
Multimodal and Python: The homepage describes vector search, full-text search, blob tables, and a Python SDK with integrations including Ray, PyTorch, and Pandas.
Storage: Documented filesystem options include local files, HDFS, Aliyun OSS, S3, Tencent COS, Azure Storage, Huawei OBS, and Google Cloud Storage.
Python client: PyPaimon provides catalog, table, Arrow, and pandas APIs plus a command-line tool; core table reads and writes do not require a JVM or a running Flink or Spark cluster.
Deployment requirement: Engine integrations require a matching connector or built-in integration and access to the catalog and warehouse storage; distributed clusters need shared storage available to participating processes.
Security model: The project says trust and authorization boundaries are generally enforced by the surrounding catalog, engine, service, operator configuration, and storage authorization.
Vulnerability reporting: The project directs users to report possible vulnerabilities privately to [email protected] and not disclose them publicly before the project responds.
Support: The project directs users to its user mailing list and GitHub issue tracker for help and issue reporting.
Analytics: The project describes petabyte-scale tables with time travel, fast scan planning, schema evolution, and incremental clustering.
Price from: Free

Where it ranks on The Geeks Club

Best Data Version Control Tools in 2026#6 of 36

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Sources

paimon.apache.org/docs/1.0/· checked 4 Oct 2026
paimon.apache.org/docs/master/ecosystem/· checked 4 Oct 2026
paimon.apache.org/docs/master/· checked 4 Oct 2026
paimon.apache.org/docs/master/maintenance/filesystems/· checked 4 Oct 2026
paimon.apache.org/docs/master/pypaimon/installation/· checked 4 Oct 2026
paimon.apache.org/docs/master/ecosystem/connecting-engine· checked 4 Oct 2026
paimon.apache.org/docs/2.0/project/security/· checked 4 Oct 2026
paimon.apache.org/docs/master/iceberg/· checked 4 Oct 2026
paimon.apache.org/security/· checked 4 Oct 2026
paimon.apache.org/docs/master/project/download/· checked 4 Oct 2026