Apache Airflow alternative — Apache Airflow vs Skippr

Apache Airflow is Apache-2.0 DAG orchestration. Latest stable is 3.3.2 (17 Sep 2026). Airflow 2.11.2 is the last 2.x; 2.x reached EOL 22 Apr 2026. 3.4.0 was still unreleased at this writing. Iceberg is apache-airflow-providers-apache-iceberg 2.1.2 (13 Oct 2026): IcebergHook (catalog introspection via PyIceberg from 2.0.0), Iceberg on AnalyticsOperator, and IcebergTableSnapshotTrigger for event-driven scheduling (2.1.0). Minimum Airflow for that provider is 2.11.0; Python 3.11+. The provider does not ingest CDC and does not write Parquet for you. Spark, Flink, Trino, or dbt tasks you put on the DAG do. Self-host is Apache-2.0; you pay the scheduler, workers, and metadata DB. Managed: Astronomer Astro (AWS us-east-1 / Azure eastus / GCP us-east1) Developer deployments from 0.35 USD/hour, Team from 0.42 USD/hour, workers from 0.13 USD/hour (A5: 1 vCPU, 2 GiB), dedicated clusters from 2.40 USD/hour. MWAA and Cloud Composer are the cloud-native SKUs. 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 scheduler replica. ELT into a warehouse is one path, not the product.

Feature	Skippr	Apache Airflow
Deploy	Single Rust binary, your VPC or on-prem	Scheduler + workers + metadata, or Astro / MWAA / Composer
Schema	In-stream evolution, sibling columns	Task XCom / the engine the operator calls
Storage	Open Iceberg on Parquet you own	None of its own. IcebergHook talks to a catalog
CDC	Log-based; fails closed if the dest cannot honour	A DAG you write. Airflow is the schedule
Query	`skipprd query` on Iceberg ∪ WAL; history in any Iceberg engine	After the task succeeds. Snapshot trigger can wake a DAG
Pricing	Free self-hosted engine	Apache-2.0. Astro from 0.35 USD/hour per deployment.

What Apache Airflow is good at

Python DAGs that already wrap the zoo: dbt, Spark, Databricks, a REST hook, a BashOperator. That is the product. IcebergTableSnapshotTrigger is why some lakes can start a DAG when a snapshot lands instead of polling. If the job is "this task after that task," that is a real product.

Where the job is wider

Airflow is a scheduler. It is not a Streamhouse. There is no Iceberg ∪ WAL. There is no skipprd query on in-flight lake rows. The Iceberg provider is a hook and a trigger, not a lake engine. Postgres CDC still lives in Debezium, DMS, or skipprd, then something on the DAG applies it. Skippr will not replace the Airflow UI or your existing operators.

The metadata database, scheduler HA, and worker pools (or Astro hours) are the ops. Skippr will not run airflow dags. dbt, Dagster, Spark, and Flink are separate pages.

If the job is "Airflow we already staff," Airflow is the narrower tool. If the job is live SQL on Iceberg you run, the DAG 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 Airflow if you want a Python scheduler, and Iceberg as a catalog hook plus the engines you already run on the DAG is enough.

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

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

Apache Airflow alternative — Apache Airflow vs Skippr ​

What Apache Airflow is good at ​

Where the job is wider ​

Getting started ​

Which to choose ​

Apache Airflow alternative — Apache Airflow vs Skippr

What Apache Airflow is good at

Where the job is wider

Getting started

Which to choose