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Kestra alternative — Kestra vs Skippr ​

Kestra is an Apache-2.0 YAML orchestrator. Flows call plugins; the engine, UI, editor, and plugins are Apache-2.0. Current LTS is 2.0 (8 Sep 2026, supported through Sep 2027). Latest listed patch is 2.0.8 (27 Oct 2026). The catalog lists 2,400+ plugins. Postgres CDC is plugin-debezium-postgres 3.2.6 (min Kestra 1.3.39): Capture writes WAL changes to Kestra internal storage; Trigger starts a flow per batch; RealtimeTrigger starts one execution per row. Each Capture task derives its own replication slot from namespace, flow, and task unless you set slotName. Iceberg has no first-party writer. Maintenance is a Trino optimize / expire_snapshots flow or a PyIceberg Python task against a REST catalog. OSS is free. Enterprise is an annual per-instance subscription (unlimited flows/tasks/executions, quote). Cloud is usage-based, request access. This is not Iceberg ∪ WAL you query in one binary.

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 per-instance EE key for ingest. ELT into a warehouse is one path, not the product.

FeatureSkipprKestra
DeploySingle Rust binary, your VPC or on-premOSS server, Enterprise Edition, or Kestra Cloud
SchemaIn-stream evolution, sibling columnsFlow inputs / the plugin. No Iceberg schema of its own
StorageOpen Iceberg on Parquet you ownInternal storage for task output. Iceberg via Trino or PyIceberg
CDCLog-based; fails closed if the dest cannot honourDebezium plugins into Kestra storage; you land the next task
Queryskipprd query on Iceberg ∪ WAL; history in any Iceberg engineAfter the task. Iceberg SQL is Trino, Athena, or a script
PricingFree self-hosted engineApache-2.0. Enterprise and Cloud are quotes.

What Kestra is good at ​

YAML flows that mix languages and 2,400+ plugins without wrapping every job as a Python DAG. That is the product. Debezium Capture plus a JDBC or script task is why some teams get WAL events into a flow without standing up Kafka Connect. If the job is "this task after that trigger," that is a real product.

Where the job is wider ​

Kestra is an orchestrator. It is not a Streamhouse. There is no Iceberg ∪ WAL. There is no skipprd query on in-flight lake rows. Debezium lands in Kestra internal storage; Iceberg still needs Trino, Spark, or PyIceberg as the next task. Skippr will not replace the Kestra UI, plugin catalog, or kestra server.

The Java runtime, the metadata store, and EE (or Cloud) are the ops. Skippr will not run Kestra flows. Airflow, Dagster, and Prefect are separate pages.

If the job is "Kestra we already staff," Kestra is the narrower tool. If the job is live SQL on Iceberg you run, the flow YAML 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
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 Kestra if you want a YAML orchestrator, and Iceberg or CDC as plugins you compose is enough.

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

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