{"categories":[{"label":"Database","url":"https://skillfed.io/packages/category/database/2"}],"enrichment":{"capability":"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.","skillfed_tags":["etl","data-integration","connectors"],"use_cases":["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."],"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\u2014including orchestration frameworks like Airflow or Dagster.\n\nThe 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\u2014not as a replacement for Airbyte's orchestration and scheduling features.","worth_installing":"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\u2014the metadata does not specify one\u2014and 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."},"id":"airbyte","links":{"html":"https://skillfed.io/packages/airbyte","md":"https://skillfed.io/packages/airbyte.md","pypi":"https://pypi.org/project/airbyte/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-08-14","license_spdx":null,"license_treatment":"unclear","name":"airbyte","python_support":"capped_below_current","summary":"PyAirbyte"},"popularity":{"monthly_downloads":955455,"position":4647,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"0.55.2"}