Argo Workflows alternative — Argo Workflows vs Skippr

Argo Workflows is an Apache-2.0 Kubernetes workflow engine (CNCF graduated). Each step is a container; you model a sequence or a DAG as a CRD. Latest supported is 4.1.6 (22 Oct 2026). 4.0.15 is also supported (same day). 3.7 reached end of support 14 Aug 2026. Artifacts go to S3, GCS, Azure, HTTP, Git, or a plugin repo. There is no first-party Iceberg writer and no CDC product. Iceberg and WAL live in the image you put on a step. OSS has no license fee; you pay the cluster, plus optional vendor support or a managed Argo SKU. 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 Workflow CRD for ingest. ELT into a warehouse is one path, not the product.

Feature	Skippr	Argo Workflows
Deploy	Single Rust binary, your VPC or on-prem	Kubernetes controller + `argo-server`
Schema	In-stream evolution, sibling columns	Container I/O / artifacts. No Iceberg schema of its own
Storage	Open Iceberg on Parquet you own	Artifact repos (S3, GCS, Azure, …). Iceberg if a step writes it
CDC	Log-based; fails closed if the dest cannot honour	A container you author. Argo is the schedule
Query	`skipprd query` on Iceberg ∪ WAL; history in any Iceberg engine	After the step succeeds. UI observes the workflow
Pricing	Free self-hosted engine	Apache-2.0. You pay Kubernetes (and optional support).

What Argo Workflows is good at

Container steps on Kubernetes so a DAG is a CRD the cluster already knows how to schedule. That is the product. Artifact passing and the argo CLI are why some platform teams put Spark, dbt, or a custom image on Argo instead of wrapping them in Airflow. If the job is "this pod after that pod," that is a real product.

Where the job is wider

Argo is a Kubernetes scheduler. It is not a Streamhouse. There is no Iceberg ∪ WAL. There is no skipprd query on in-flight lake rows. Iceberg still starts in Spark, Flink, or PyIceberg inside a container. Postgres CDC still starts in Debezium or skipprd. Skippr will not replace the Argo UI, Workflow CRDs, or argo submit.

The cluster, the controller, and artifact buckets are the ops. Skippr will not run Argo. Airflow, Flyte, and Prefect are separate pages.

If the job is "Argo we already staff," Argo is the narrower tool. If the job is live SQL on Iceberg you run, the CRD 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 Argo Workflows if you want Kubernetes DAGs, and Iceberg or CDC as containers you already run is enough.

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

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

Argo Workflows alternative — Argo Workflows vs Skippr ​

What Argo Workflows is good at ​

Where the job is wider ​

Getting started ​

Which to choose ​

Argo Workflows alternative — Argo Workflows vs Skippr

What Argo Workflows is good at

Where the job is wider

Getting started

Which to choose