dagster-airbyte
Package for integrating Airbyte with Dagster.
Decision gist · record as of 2026-08-14
Yes. The package has low install friction, active maintenance, no known vulnerabilities, and a permissive license. Install it if you are already using Dagster for orchestration and want to integrate Airbyte connectors as declarative assets; it is not necessary if you are not using Dagster or do not need Airbyte integration.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or later (up to 3.14).
- Assumes Airbyte instance is running and accessible; Dagster and its dependencies must be installed.
- Low install friction with a pure-wheel distribution.
License · maintenance · safety
Apache-2.0 (permissive) — Apache-2.0 permissive license allows use in commercial and proprietary projects without restriction, though derivative works must retain the license notice.
last release 2026-08-14 (0 days) · last repo commit 2026-08-14 · 15,996 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 206,459 downloads/mo, #9,572 on PyPI
Alternatives
Verify before relying
pip install dagster-airbyte
import dagster as dg
from dagster_airbyte import airbyte_asset
@airbyte_asset
def my_data_from_airbyte():
pass- Specific Airbyte connector types and versions supported by this integration version
- Whether Airbyte instance discovery/configuration is automatic or requires manual setup
- Performance characteristics when orchestrating large-scale Airbyte syncs
What it is and what it does
dagster-airbyte bridges Airbyte's data connectors with Dagster's declarative asset model, allowing you to define data ingestion workflows as Python functions that Dagster orchestrates. It depends on dagster, python-dateutil, and requests, and is designed to fit into Dagster's broader data pipeline orchestration framework—letting you compose Airbyte-sourced data with transformations and other assets in a single, testable codebase.
The package is part of Dagster's growing integration library and is maintained alongside the core Dagster project. It supports Python 3.10 through 3.14 and carries an Apache-2.0 license, making it suitable for both open-source and commercial use.
Use it for
- Define Airbyte data connectors as Dagster assets to orchestrate recurring data ingestion alongside downstream transformations.
- Build multi-stage data pipelines that combine Airbyte ingestion with Dagster-orchestrated transformations and ML model training.
- Centralize observability and lineage tracking for data flows that start with Airbyte connectors using Dagster's web UI.
- Test data ingestion logic locally during development before deploying to production orchestration.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
The package has low install friction, active maintenance, no known vulnerabilities, and a permissive license. Install it if you are already using Dagster for orchestration and want to integrate Airbyte connectors as declarative assets; it is not necessary if you are not using Dagster or do not need Airbyte integration.
Install
dagster-airbyte on PyPI
Before you install
Low install friction with a pure-wheel distribution. Actively maintained with a recent release and strong upstream project health (15996 GitHub stars, active repository).
Requires Python 3.10 or later (up to 3.14). Assumes Airbyte instance is running and accessible; Dagster and its dependencies must be installed.
License in practice
Apache-2.0 permissive license allows use in commercial and proprietary projects without restriction, though derivative works must retain the license notice.
Quickstart
pip install dagster-airbyte
import dagster as dg
from dagster_airbyte import airbyte_asset
@airbyte_asset
def my_data_from_airbyte():
passVerify before relying
- Specific Airbyte connector types and versions supported by this integration version
- Whether Airbyte instance discovery/configuration is automatic or requires manual setup
- Performance characteristics when orchestrating large-scale Airbyte syncs
Package facts
| License | Apache-2.0 permissive |
| Python support | Supports the current Python release <3.15,>=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 3 packagesdagsterpython-dateutilrequests |
| Maintenance | Actively maintained 0 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 206,459 / month, #9,572 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
Evidence: dagster_airbyte-0.29.18-py3-none-any.whl
Tags
Let your AI agent find packages like this
Example. Real query, live index.
You found this page by searching. An agent finds it by wishing: SkillFed indexes 14,416 PyPI packages by what they can do, searchable in plain language.
wish › “airbyte dagster integration”
- dagster-airbyteIntegrates Airbyte data connectors with Dagster's orchestration…
- airbytePyAirbyte lets you run Airbyte data connectors directly from Python…
- airbyte-protocol-models-pdv2Provides Python models and serialization for the Airbyte Protocol,…
Give your agent the search over MCP, or paste the wish link into any chat.
More Distributed Computing packages
gRPC Python is an HTTP/2-based RPC framework that enables you to define and call remote procedures across network boundaries using protocol buffers for serialization.
Install it if you need RPC communication in a distributed system or are integrating with existing gRPC services.
execnet lets you spawn and communicate with Python interpreters across local processes, remote hosts, and different platforms, using a simple API for task distribution and inter-process messaging.
However, the aging maintenance status (275 days since last release) means you should verify it meets your concurrency and performance needs before committing to a…
Cloudpickle extends Python's standard pickle module to serialize lambda functions, interactively-defined functions and classes, and other constructs that the default pickle cannot handle, making it suitable for cluster computing and remote code execution.
Install it if you need to serialize lambda functions, interactively-defined code, or non-standard Python constructs for cluster computing or distributed execution.
Provides a unified, open()-compatible Python API for streaming large files from remote storage (S3, GCS, Azure, HDFS, SFTP, HTTP) and local filesystems, with transparent compression support.
Install it if you work with large files on cloud storage or remote systems and want to avoid writing boilerplate around multiple SDKs.
Portalocker provides cross-platform file locking with support for exclusive and shared locks, plus Redis-based distributed locks and process-aware PID file locking.
Install it if you need file or process coordination; the optional extras (pywin32, redis) are only required for specific lock types.
Ray is a distributed computing framework that scales Python applications from a single machine to multi-node clusters, providing abstractions for parallel tasks, stateful actors, and shared objects.
See also dagster · dagster-fivetran · dagster-dlt · airbyte · dagster-dg-core · airbyte-api · dagster-embedded-elt · dagster-aws · dagster-spark · airbyte-source-declarative-manifest