Mage alternative — Mage vs Skippr

Mage is a notebook-style pipeline tool. OSS is Apache-2.0; latest on PyPI is 0.9.80 (9 Oct 2026), Python 3.10+, dbt 1.10 adapters in that cut. You build blocks in Python, SQL, or R, schedule them, and run data integrations. Iceberg is Mage Pro: SQL catalog (Postgres-backed) or AWS Glue, append / overwrite, pyiceberg scans (row_filter, snapshot_id). The Iceberg S3 data-integration destination is also Pro-only. Postgres CDC is documented as logical replication (pgoutput, replication_slot, publication_name). Mage Pro Starter is 29 USD/month plus 0.0167 USD per execution minute (1 seat); Team is 100 USD/month plus the same minute rate (up to 10 seats). A billed minute is 1 CPU and 4 GiB RAM; larger shapes use proportionally more minutes. 7-day trial. Enterprise is a quote (private, hybrid, on-prem). 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 execution-minute line. ELT into a warehouse is one path, not the product.

Feature	Skippr	Mage
Deploy	Single Rust binary, your VPC or on-prem	OSS Docker / pip, or Mage Pro (managed, hybrid, on-prem)
Schema	In-stream evolution, sibling columns	Block output. Iceberg schema from the Pro I/O
Storage	Open Iceberg on Parquet you own	Warehouse dests in OSS. Iceberg S3 is Mage Pro
CDC	Log-based; fails closed if the dest cannot honour	Postgres logical replication as a source you configure
Query	skipprd query on Iceberg ∪ WAL; history in any Iceberg engine	After the block. Pro Iceberg load is a scan, not a lake WAL
Pricing	Free self-hosted engine	Apache-2.0 OSS. Pro Starter from 29 USD/month + minutes.

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What Mage is good at

A visual block editor so a Python or SQL pipeline is a notebook you can rerun from the failed block. That is the product. dbt inside the same UI, and Pro Iceberg load/export when you pay for Pro, are why some lakes get a Mage project instead of a DAG of bash. If the job is "this block onto this dest," that is a real product.

Where the job is wider

Mage is a pipeline UI. It is not a Streamhouse. There is no Iceberg ∪ WAL. There is no skipprd query on in-flight lake rows. Iceberg write is Mage Pro, not OSS. Postgres CDC still lands wherever the next block writes; Skippr will not replace Mage Pro, the notebook editor, or mage start.

The scheduler, the Pro cluster, and execution minutes are the ops. Skippr will not run Mage blocks. Airflow, Dagster, and dbt are separate pages.

If the job is "Mage we already staff," Mage is the narrower tool. If the job is live SQL on Iceberg you run, the notebook 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 Mage if you want a notebook pipeline UI, and a warehouse dest or Mage Pro Iceberg is enough.

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

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