Apache SeaTunnel alternative — Apache SeaTunnel vs Skippr

Apache SeaTunnel is a distributed extract-load engine (they write EtLT). One HOCON job file. The same definition can run on Zeta (their engine), Apache Flink, or Apache Spark. Modes are batch, streaming, and CDC. Checkpoints give exactly-once when the source and sink both honour it. Schema changes can propagate without a pause. The homepage counts about 200 connectors: JDBC and CDC databases, Kafka and Pulsar, files, Iceberg, Hudi, Paimon, Delta, and a long China OLAP list (ClickHouse, Doris, StarRocks). Iceberg source and sink document version 1.6.1, CDC writes, and multi-table jobs. The project is an Apache TLP. You pay your cluster.

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 Zeta, Flink, or Spark to pick. ELT into a warehouse is one path, not the product.

Feature	Skippr	Apache SeaTunnel
Deploy	Single Rust binary, your VPC or on-prem	Zeta process, or a Flink / Spark cluster. HOCON job file
Schema	In-stream evolution, sibling columns	Detects source change and can propagate it. Connector-dependent
Storage	Open Iceberg on Parquet you own	Whatever sink you named. Iceberg is one of several lake tables
CDC	Log-based; fails closed if the dest cannot honour	Snapshot then binlog / WAL on supported databases. Engine-specific
Query	skipprd query on Iceberg ∪ WAL; history in any Iceberg engine	Destination after the job. SQL transform is a row pass, not a lake
Pricing	Free self-hosted engine	Apache-2.0, pay the engine you chose. See their docs.

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What Apache SeaTunnel is good at

One job file, three engines. If you already run Flink for large streaming state, or Spark for a heavy shuffle, you keep that runtime and reuse the connector. Zeta is the lighter default. Parallel split reads, snapshot-then-CDC, and checkpoint restart are named mechanisms. Iceberg, Hudi, Paimon, and Delta are first-class sinks. If the dest is Doris or StarRocks, this is a well-trodden path. That is a real integration engine.

Where the job is wider

SeaTunnel is EtLT. It is not a Streamhouse. There is no Iceberg ∪ WAL. There is no skipprd query on in-flight rows. The SQL transform is a per-row expression. Heavy models stay in the warehouse.

Engine choice is also the ops. Acceldata's SeaTunnel notes say Zeta is the wrong pick when streaming state is huge (they cite Flink plus RocksDB past about 100 TB) or when the SQL needs Spark's planner. Exactly-once is "compatible source and sink," not a blanket. Iceberg here is 1.6.1 in their connector docs; check that against the catalog you already run.

Skippr will not give you 200 connectors, a Flink cluster, or Doris / StarRocks as a native sink. If the job is "move these tables onto a cluster we already operate," SeaTunnel is the narrower tool. If the job is live SQL on Iceberg you run, SeaTunnel 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.

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

CLI

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")

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 Apache SeaTunnel if you want one HOCON job on Zeta, Flink, or Spark, and a warehouse or lake table after the write is enough.

Choose Skippr if you want one binary, live SQL, and Iceberg files that stay yours. Skippr will not replace a SeaTunnel cluster.

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