apache-airflow-providers-apache-iceberg
Provider package apache-airflow-providers-apache-iceberg for Apache Airflow
Decision gist · record as of 2026-08-14
Yes, if you run Apache Airflow and use Apache Iceberg. The package is actively maintained, has low install friction, carries a permissive license, and is backed by the Apache project. Install it when you need to orchestrate Iceberg operations within Airflow workflows; skip it if you don't use Airflow or Iceberg.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires apache-airflow >=2.11.0, apache-airflow-providers-common-compat >=1.8.0, and pyiceberg >=0.8.0; Python 3.10 or later.
- Low install friction; pure Python wheel with three runtime dependencies.
- Actively maintained with a recent release (68 days old) and backed by the Apache Airflow project's active repository.
License · maintenance · safety
Apache-2.0 (permissive) — Apache-2.0 permissive license allows commercial and private use with minimal restrictions; suitable for most production environments.
last release 2026-06-07 (68 days) · last repo commit 2026-08-14 · 46,490 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 1,611,773 downloads/mo, #3,718 on PyPI
Alternatives
Verify before relying
pip install apache-airflow-providers-apache-iceberg
from airflow.providers.apache.iceberg.operators import IcebergOperator
# Use within an Airflow DAG to orchestrate Iceberg operations- What specific Iceberg operations (create, insert, update, delete, merge) the operators support
- Whether the provider includes sensors for monitoring Iceberg table state or job completion
- Performance characteristics when orchestrating large-scale Iceberg workloads
What it is and what it does
This is an Apache Airflow provider package that bridges Airflow's orchestration framework with Apache Iceberg, a table format designed for large-scale analytics. It supplies operators and hooks that allow you to define Iceberg-related tasks within Airflow DAGs—for example, creating tables, inserting or updating data, or triggering transformations on Iceberg datasets. The package is maintained as part of the official Airflow ecosystem and depends on pyiceberg for the underlying Iceberg protocol implementation.
You install it alongside an existing Airflow deployment (version 2.11.0 or later) and import its operators into your DAG definitions. It requires Python 3.10 or later and has no known vulnerabilities. The package is actively maintained and positioned for production use, making it suitable for data teams building Airflow-orchestrated analytics pipelines that use Iceberg as their table format.
Use it for
- Orchestrate Iceberg table creation and schema evolution as part of multi-step data pipeline DAGs
- Schedule recurring data ingestion or transformation jobs that read from or write to Iceberg tables
- Coordinate Iceberg operations across multiple data sources and downstream analytics tools in a single workflow
- Monitor and manage Iceberg table state changes as part of larger data lake orchestration
- Integrate Iceberg workloads with Airflow's scheduling, retry logic, and alerting infrastructure
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you run Apache Airflow and use Apache Iceberg.
The package is actively maintained, has low install friction, carries a permissive license, and is backed by the Apache project. Install it when you need to orchestrate Iceberg operations within Airflow workflows; skip it if you don't use Airflow or Iceberg.
Install
apache-airflow-providers-apache-iceberg on PyPI
Before you install
Low install friction; pure Python wheel with three runtime dependencies. Actively maintained with a recent release (68 days old) and backed by the Apache Airflow project's active repository.
Requires apache-airflow >=2.11.0, apache-airflow-providers-common-compat >=1.8.0, and pyiceberg >=0.8.0; Python 3.10 or later.
License in practice
Apache-2.0 permissive license allows commercial and private use with minimal restrictions; suitable for most production environments.
Quickstart
pip install apache-airflow-providers-apache-iceberg
from airflow.providers.apache.iceberg.operators import IcebergOperator
# Use within an Airflow DAG to orchestrate Iceberg operationsVerify before relying
- What specific Iceberg operations (create, insert, update, delete, merge) the operators support
- Whether the provider includes sensors for monitoring Iceberg table state or job completion
- Performance characteristics when orchestrating large-scale Iceberg workloads
Package facts
| License | Apache-2.0 permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 3 packagesapache-airflowapache-airflow-providers-common-compatpyiceberg |
| Maintenance | Actively maintained 68 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 1,611,773 / month, #3,718 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 5 - Production/StableEnvironment :: ConsoleEnvironment :: Web EnvironmentFramework :: Apache AirflowFramework :: Apache Airflow :: ProviderIntended Audience :: DevelopersIntended Audience :: System AdministratorsProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Topic :: System :: Monitoring |
Evidence: apache_airflow_providers_apache_iceberg-2.0.3-py3-none-any.whl
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See also apache-airflow-providers-zendesk · pyiceberg · pyiceberg-core · apache-airflow-providers-standard · apache-airflow-providers-apache-pinot · apache-airflow-providers-arangodb · apache-airflow-providers-tableau · apache-airflow-providers-cloudant · apache-airflow-providers-neo4j · bauplan