Apache Paimon alternative — Apache Paimon vs Skippr

Apache Paimon is an Apache-2.0 lake format aimed at streaming lakes, especially Flink. Current release is 2.1.0 (12 Oct 2026), with PyPaimon 2.1.0 on PyPI. Primary-key tables are an LSM: L0 absorbs writes, compaction merges levels. Append tables skip the merge. CDC ingestion is Flink actions (paimon-flink-action-2.1.0.jar plus paimon-flink-cdc): MySQL, PostgreSQL, MongoDB, Kafka, Pulsar, or a Flink DataStream. Schema evolution on that path is column add and compatible type change, not drop or rename. Iceberg compatibility (metadata.iceberg.storage, default disabled) publishes Iceberg manifests that point at existing Paimon files. It does not copy data. Append tables become visible after publication. Primary-key tables without Iceberg deletion vectors become visible to Iceberg readers after files reach the highest LSM level (full compaction). With Iceberg v3 deletion vectors, files above L0 plus DVs. Writes, schema, compaction, and snapshot expiry stay in Paimon. Independent Iceberg mutations can break Paimon's view of the shared files. There is no licence fee. You pay Flink (or Spark), the warehouse, and people. This is not Iceberg ∪ WAL. Serving freshness for Iceberg readers is compaction plus publication.

Skippr is an open Streamhouse you run as one Rust binary. Write skippr.yml (or the same thing in Python), discover, sync. You query live state over Iceberg ∪ WAL, and the history is ordinary Iceberg in your bucket. No Flink session. ELT into a warehouse is one path, not the product.

Feature	Skippr	Apache Paimon
Deploy	Single Rust binary, your VPC or on-prem	Flink/Spark jobs plus a Paimon warehouse
Schema	In-stream evolution, sibling columns	CDC actions: add and some type changes. Not drop/rename
Storage	Open Iceberg on Parquet you own	Paimon LSM/append files. Iceberg is published metadata
CDC	Log-based; fails closed if the dest cannot honour	Flink CDC actions (Postgres, MySQL, Mongo, Kafka, Pulsar)
Query	`skipprd query` on Iceberg ∪ WAL; history in any Iceberg engine	Flink/Spark/Paimon. Iceberg readers after compaction/publication
Pricing	Free self-hosted engine	Apache-2.0. Pay Flink and object storage.

What Apache Paimon is good at

Flink streaming upserts into a lake table with an LSM, then (optionally) Iceberg readers on the compacted files. That is the product. Postgres CDC as a Paimon action, without a second Connect cluster, is why some Flink estates pick it. If the job is "this Flink changelog into this Paimon table," that is a real product.

Where the job is wider

Paimon is a table format plus Flink runtime. It is not a Streamhouse. There is no Iceberg ∪ WAL. There is no skipprd query on in-flight Iceberg rows. Iceberg is a compatibility layer over Paimon files. Primary-key freshness for Iceberg engines waits on compaction. You must not write the Iceberg view with a second engine.

Flink math is the ops. Action JARs, checkpoints, LSM levels, and catalog publication. Skippr will not write Paimon. Skippr also will not replace a Paimon primary-key table that already serves Flink SQL. Flink CDC is a separate page.

If the job is "Paimon on Flink we already staff," Paimon is the narrower tool. If the job is live SQL on Iceberg you run, the LSM was never that.

Getting started

Install skipprd from the install guide, or pip install skippr. The three tabs write the same skippr.yml. Then discover, sync, and query.

YAMLCLIPython

yaml

skippr:
  workspace: analytics
  skipprd_el_storage_mode: local

pipelines:
  orders:
    data_source: data_sources.app_db
    data_sink: data_sinks.lake

data_sources:
  app_db:
    Postgres:
      host: db.internal
      user: skippr_reader
      password: ${APP_DB_PASSWORD}
      database: app
      tables: [orders, customers]
      cdc_mode: snapshot_then_cdc

data_sinks:
  lake:
    AthenaIceberg:
      warehouse: s3://my-iceberg-warehouse/
      glue_database_name: analytics
      athena_workgroup_name: primary
      athena_results_s3_bucket: my-athena-results
      region: us-east-1
    schema_sink: schema_sinks.lake_schema

schema_sinks:
  lake_schema:
    AthenaIceberg:
      warehouse: s3://my-iceberg-warehouse/
      glue_database_name: analytics
      athena_workgroup_name: primary
      athena_results_s3_bucket: my-athena-results
      region: us-east-1

bash

skipprd --workspace analytics --storage-mode local connect data-source postgres \
  --pipeline orders \
  --name app_db \
  --host db.internal \
  --user skippr_reader \
  --password '${APP_DB_PASSWORD}' \
  --database app \
  --tables orders \
  --tables customers \
  --cdc-mode snapshot_then_cdc

skipprd connect data-sink athenaiceberg \
  --pipeline orders \
  --name lake \
  --warehouse s3://my-iceberg-warehouse/ \
  --glue-database-name analytics \
  --athena-workgroup-name primary \
  --athena-results-s3-bucket my-athena-results \
  --region us-east-1

skipprd connect schema-sink athenaiceberg \
  --pipeline orders \
  --name lake_schema

python

from skippr import (
    Config,
    DataSinkAthenaIceberg,
    DataSourcePostgres,
    DataSourcePostgresSourceCdcMode,
    EnvRef,
    LocalStorage,
    Pipeline,
)

cfg = Config().workspace("analytics").storage(LocalStorage())

app_db = cfg.data_source(
    "app_db",
    DataSourcePostgres(
        host="db.internal",
        user="skippr_reader",
        password=EnvRef("APP_DB_PASSWORD"),
        database="app",
        tables=["orders", "customers"],
        cdc_mode=DataSourcePostgresSourceCdcMode.SNAPSHOTTHENCDC,
    ),
)

lake = cfg.data_sink(
    "lake",
    DataSinkAthenaIceberg(
        warehouse="s3://my-iceberg-warehouse/",
        glue_database_name="analytics",
        athena_workgroup_name="primary",
        athena_results_s3_bucket="my-athena-results",
        region="us-east-1",
    ),
    schema_sink="lake_schema",
)

cfg.pipeline("orders", Pipeline(data_source=app_db, data_sink=lake))
cfg.save("skippr.yml")

bash

export APP_DB_PASSWORD='your-postgres-password'
skipprd discover --pipeline orders
skipprd sync --pipeline orders --once
skipprd query "SELECT count(*) FROM analytics.orders"

Which to choose

Choose Paimon if you already run Flink, and a Paimon primary-key table is the lake.

Choose Skippr if you want one binary, live SQL on Iceberg ∪ WAL, and files that stay yours. Skippr will not run a Paimon warehouse.

Install from Install. Connector and query reference live in Developer Docs. For optional onboarding in your VPC, Enterprise Jumpstart or Contact.

Apache Paimon alternative — Apache Paimon vs Skippr ​

What Apache Paimon is good at ​

Where the job is wider ​

Getting started ​

Which to choose ​

Apache Paimon alternative — Apache Paimon vs Skippr

What Apache Paimon is good at

Where the job is wider

Getting started

Which to choose