Airbyte is an open-source data movement platform for ELT pipelines that provides over 600 connectors and can be self-hosted or used via cloud. It moves data from various APIs, databases, and files into data warehouses, data lakes, and AI applications.
Project overview
With over 600 connectors and the ability for data engineers to customize existing connectors for niche data sources, Airbyte addresses the broad challenge of consolidating disparate data streams through an open-source, self-hostable architecture.
Project type
Data Processing · Infrastructure
Use cases
Data Analysis
Deployment
Refer to project documentation
License
License pending
Best for
Data teams and developers building ELT pipelines who need to move data from various APIs, databases, and files to analytical or AI destinations.
Key capabilities
Provides a catalog of over 600 connectors for various APIs, databases, data warehouses, data lakes, and AI applications.
Getting started
Deployment requires deploying the platform or setting up Airbyte Cloud. After deployment, users create connectors using the no-code Connector Builder or low-code CDK, and orchestrate Airbyte syncs. The platform interacts via an API.
Evidence and sources
README: Open-source data movement for ELT pipelines and AI agents — from APIs, databases & files to warehouses, lakes, and AI applications
README: - **Moving data into warehouses, lakes, or databases (ELT / ETL)** → use [Airbyte Open Source](https://docs.airbyte.com/quickstart/deploy-airbyte) (this repo) or [Airbyte Cloud](h…
Release: Airbyte 2.0
README: For moving data into warehouses, lakes, and databases: - [Deploy Airbyte Open Source](https://docs.airbyte.com/quickstart/deploy-airbyte) or set up [Airbyte Cloud](https://cloud.a…
README: Or install the open-source [Agent SDK](https://github.com/airbytehq/airbyte-agent-sdk): `uv pip install airbyte-agent-sdk`. Works with pydantic-ai, LangChain, OpenAI Agents, and F…