Apache Paimon

Overview

Apache Paimon is an open-source table format for teams building data lakehouses that need to process changing records alongside append-heavy data. It is best suited to data engineers working with streaming and batch pipelines; its main trade-off is that teams must assemble and operate the engines, catalog, and storage around it.

Key features

Two table patterns for different workloads

Primary-key tables support large-scale updates through configurable merge engines for deduplication, partial updates, aggregation, and first-row updates. Their LSM structure and changelog producers make them a fit for streaming changes. Append tables, meanwhile, support batch and streaming loads, with automatic small-file merging and compaction using Z-order sorting. That separation is useful when a lakehouse needs both mutable records and high-volume append workflows, but choosing the right table pattern is part of the design work.

History and large-table management

ACID transactions, time travel, schema evolution, and table-level snapshots provide a foundation for point-in-time rollback and dataset branching. Paimon also targets petabyte-scale tables and many partitions, with fast scan planning and incremental clustering for analytics. Those capabilities make it more than a basic file layout, but they do not eliminate the need to plan the surrounding compute and storage.

Engine, ingestion, and Python integrations

Documented compute integrations include Flink, Spark, Hive, Trino, Presto, StarRocks, and Doris, while CDC pipelines cover MySQL, PostgreSQL, Kafka, MongoDB, and Pulsar. Supported read, write, and table operations vary by engine and version, so compatibility should be checked against the intended deployment. PyPaimon offers catalog, table, Arrow, and pandas APIs plus a command-line tool; core table reads and writes do not require a JVM or an active Flink or Spark cluster. The project also describes vector and blob storage, full-text search, global indexing, and Python integrations with Ray, PyTorch, and Pandas for AI and multimodal workloads.

Storage and deployment

Filesystem options include local files, HDFS, Aliyun OSS, S3, Tencent COS, Azure Storage, Huawei OBS, and Google Cloud Storage. Engine deployments need a matching connector or built-in integration and access to the catalog and warehouse; distributed clusters also need shared storage accessible to their participating processes. Download options include engine JARs, filesystem plugins, and a Java API bundle, giving teams routes to run Paimon with query engines or embed it in a Java application. This flexibility is valuable in varied infrastructure, but it comes with integration and operations work rather than a managed end-to-end service.

Security and support

Authorization boundaries generally come from the surrounding catalog, engine, service, operator configuration, and storage permissions. The filesystem guide documents OSS server-side encryption using AES256, KMS, or SM4. Users can seek help through the mailing list and GitHub issue tracker; possible vulnerabilities should be reported privately to [email protected] before public disclosure.

Pricing

Apache Paimon is free and open source. Its Apache Paimon plan costs 0.00 USD per free and provides an open-source data lake platform; users choose connectors and storage plugins for their environment. There are no paid plan tiers in this offering, but the free price does not include a turnkey deployment: teams still need compatible compute, catalog access, and storage. Bring-your-own storage and table-level snapshot granularity are central considerations for budgeting and architecture.

Platforms

Paimon supports API, Linux, macOS, self-hosted, and Windows environments. It is a data platform rather than a typical end-user device app, and practical compatibility depends on the selected engine integration and storage setup.

Who it's for

Paimon is a strong fit for engineering teams building lakehouses that need streaming updates, batch processing, table history, and choice among documented engines and storage systems. It is especially relevant when primary-key and append workloads must coexist, or when Python-based access and multimodal data features matter. Teams seeking a managed product with a single packaged compute and storage service should look elsewhere; Paimon expects users to configure and operate its surrounding stack.

Pros and cons

• Pros: Primary-key and append table patterns cover different update and ingestion needs, rather than forcing one workflow on both.
• Pros: Transactions, time travel, schema evolution, branching, and rollback support table management and analytical history at large scale.
• Pros: Broad documented engine, CDC, filesystem, and Python integrations give teams options across established data stacks.
• Cons: Engine operation support and version compatibility vary, so a connector match must be verified before deployment.
• Cons: Teams must provide catalog access, connectors, and warehouse storage; distributed deployments also require shared storage.
• Cons: Security authorization depends largely on the surrounding services and storage configuration, adding responsibility for operators.

Alternatives

Data Version Control Tools is a broader category to compare when choosing among data versioning approaches.

Weights & Biases is worth considering when the priority is model and experiment workflows; its Free plan is 0.00 USD per month and includes five model seats, 5 GB/mo storage, and 1 GB/mo Weave data ingestion.

Dolt is an alternative for teams wanting a free, open-source program of about 100 MB; its DoltHub Pro plan is 5.00 USD per month.

ClearML is an alternative with a self-hosted version described as 100% open source on GitHub.

Neon is a freemium option for hosted database branching, with a Free plan that includes 10 projects, 50 CU-hours/month per project, 0.5 GB storage per project, 10 branches per project, and 5 GB/month egress.

Nile is a local option that runs entirely on the user's machine without requiring a cloud account.

DataLad is another free and open-source option for Linux, macOS, and Windows.

Hugging Face Hub may suit users focused on sharing model and dataset assets; its Free user or org plan includes 100GB private storage, with public storage best-effort.

Roboflow is a freemium alternative for computer-vision workflows, with a Free Tier including 10 credits a month.

Verdict

Choose Apache Paimon if your team needs an open-source lake format that can combine streaming updates, batch tables, table history, and multiple documented engine integrations. Its strongest reason to choose it is the breadth of table and workload support; its strongest reason to look elsewhere is the need to assemble and operate compatible engines, catalog, and storage yourself.