Dagster alternative — Dagster vs Skippr

Dagster is an Apache-2.0 orchestrator. You declare software-defined assets; the daemon materializes them. Latest core is 1.13.28 (libraries 0.29.28, 30 Oct 2026). Iceberg is dagster-iceberg: community-supported, API in preview (breaking changes in patch releases, not production-ready). I/O managers for PyArrow, Daft, pandas, Polars, and Spark Connect. Write modes append, overwrite (default), upsert with join_cols. The catalog namespace must already exist. CDC is whatever asset you write, not Dagster. Self-host is Apache-2.0. Dagster+ Solo is 10 USD/month plus 0.040 USD per credit; Starter is 100 USD/month plus 0.035 USD per credit. A credit is one asset materialization or one op. Serverless compute 0.010 USD/minute; Hybrid has no serverless compute charge. 30-day trial. Pro is a quote. 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 credit per materialization. ELT into a warehouse is one path, not the product.

Feature	Skippr	Dagster
Deploy	Single Rust binary, your VPC or on-prem	OSS daemon, or Dagster+ (Serverless / Hybrid)
Schema	In-stream evolution, sibling columns	Asset keys. Iceberg schema from the I/O manager
Storage	Open Iceberg on Parquet you own	Wherever the I/O manager writes. Iceberg is preview
CDC	Log-based; fails closed if the dest cannot honour	An asset you author. Dagster schedules it
Query	skipprd query on Iceberg ∪ WAL; history in any Iceberg engine	After materialization. UI observes the asset
Pricing	Free self-hosted engine	Apache-2.0. Dagster+ Solo from 10 USD/month + credits.

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What Dagster is good at

Assets with lineage, partitions, and checks, so a Python team can see what is stale without reading every cron. That is the product. dagster-dbt and the Iceberg I/O managers (when they leave preview) are why some lakes get an asset graph instead of a DAG of bash. If the job is "this asset onto this catalog," that is a real product.

Where the job is wider

Dagster is an orchestrator. It is not a Streamhouse. There is no Iceberg ∪ WAL. There is no skipprd query on in-flight lake rows. dagster-iceberg is preview and community-supported. Postgres CDC still starts in a connector you wire as an asset. Skippr will not replace Dagster UI, sensors, or dagster-dbt.

The daemon (or Dagster+ credits) is the ops. Skippr will not run dagster dev. dbt and Airflow are separate pages.

If the job is "Dagster we already staff," Dagster is the narrower tool. If the job is live SQL on Iceberg you run, the asset graph 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 Dagster if you want an asset orchestrator, and a preview Iceberg I/O manager or a warehouse dest is enough.

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

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