airbyte

What it is and what it does

PyAirbyte is a Python library that brings Airbyte's connector ecosystem into your code without requiring the full Airbyte platform. It lets you instantiate data sources (like Postgres, Shopify, GA4, or CoinAPI) and destinations (like Snowflake or DuckDB) as Python objects, then extract and load data using simple method calls. You can cache data locally, transform it with pandas or other tools, and integrate the whole pipeline into existing Python workflows—including orchestration frameworks like Airflow or Dagster.

The library supports both declarative YAML-based connectors and Python-based ones. For Python connectors, it defaults to using `uv` for fast installation, with fallback to `pip`, and lets you specify custom Python versions per connector if needed. You can also run connectors in Docker for better isolation and dependency management. It's designed for developers building ETL pipelines, testing connectors, or embedding data integration into applications—not as a replacement for Airbyte's orchestration and scheduling features.

Use it for:

• Extract data from a SaaS API (Shopify, GA4, GitHub) and load it into a data warehouse (Snowflake, BigQuery) or local cache in a Python script.
• Build a lightweight ETL pipeline in Airflow or Dagster that pulls from multiple sources and lands data in your preferred destination.
• Test and develop new Airbyte connectors locally using Python before publishing them.
• Combine Airbyte connectors with pandas transformations in a Jupyter notebook for ad-hoc data analysis and export.
• Cache incremental data syncs locally (e.g., Postgres to DuckDB) and query them with SQL in the same Python process.

Worth the install?

AI-flagged interpretation of the facts on this page — verify before relying

PyAirbyte lets you run Airbyte data connectors directly from Python code, extracting and loading data from hundreds of sources without needing the full Airbyte platform.

Yes, if you need to run Airbyte connectors from Python and accept the license ambiguity. The library is actively maintained, has low install friction, and integrates well with existing Python data tools. However, verify the license terms first—the metadata does not specify one—and be aware that the 37 runtime dependencies may add setup complexity in constrained environments. Not a replacement for full Airbyte if you need orchestration, scheduling, or production monitoring.

Install

airbyte on PyPI

pip install airbyte

uv add airbyte

poetry add airbyte

Installing airbyte

Before you install

Low install friction with a pure-Python wheel. Active maintenance as of release date. Requires Python 3.10–3.12; note that 37 runtime dependencies including database drivers (psycopg, snowflake-connector-python, duckdb) and cloud SDKs (google-cloud-bigquery, etc.) will be pulled in, which may add setup time depending on your environment.

License in practice

License treatment is unclear; no SPDX identifier or raw license text is available in the metadata. Verify the actual license before use in proprietary or restricted contexts.

Quickstart

pip install airbyte

import airbyte as ab

source = ab.get_source('source-postgres', config={...})
dest = ab.get_destination('destination-snowflake', config={...})
ab.run(source, dest)

Requires Python 3.10–3.12. Connectors may need additional system libraries (e.g., libpq for Postgres) or Docker if using docker_image=True. Cloud destination credentials must be configured via environment variables or config dicts.

Verify before relying

• Exact license terms and any restrictions on commercial use or redistribution.
• Whether all 37 runtime dependencies are truly required for basic use or if subsets can be installed conditionally.
• Compatibility and stability with orchestration frameworks (Airflow, Dagster, Snowpark) mentioned in the description.

Package facts

Evidence: airbyte-0.55.2-py3-none-any.whl

Tags

More Database packages