Orchestration-pipelines

A library for defining and generating Apache Airflow DAGs declaratively using YAML. Currently focused on orchestration of GCP resources (Dataproc, BigQuery, Dataform) and DBT...

Overview

orchestration-pipelines allows you to define complex data workflows in simple, human-readable YAML files. It abstracts away the boilerplate of writing Airflow DAGs in Python, making it easier for non-Python experts to create and manage pipelines.

• Documentation: docs/doc.md
• Changelog: CHANGELOG.md
• Limitations: docs/limitations.md

Supported Python Versions

Python >= 3.10

Features

• Declarative DAGs: Define your pipeline structure, triggers, and actions in YAML.
• Rich Actions Support: Built-in support for:
  • Python Scripts
  • Google Cloud BigQuery
  • Google Cloud Dataproc (Serverless, Ephemeral and existing clusters)
  • Google Cloud Dataform
  • DBT
• Automatic Generation: A simple Python call generates the full Airflow DAG.
• Versioning: Supports versioning of pipelines via a manifest file(as of Preview, on Google Cloud Composer).

Installation

You can install orchestration-pipelines from PyPI:

pip install orchestration-pipelines

Quick Start

1. Define your pipeline in YAML

Create a file named my_pipeline.yml:

modelVersion: "1.0"
pipelineId: "my_pipeline"
description: "A simple example pipeline"
runner: "airflow"

defaults:
  projectId: "your-gcp-project"
  location: "us-central1"

triggers:
  - schedule:
      interval: "0 4 * * *"
      startTime: "2026-01-01T00:00:00"
      catchup: false

actions:
  - sql:
      name: "create_table"
      query:
        inline: "CREATE TABLE IF NOT EXISTS `your-gcp-project.my_dataset.my_table` (id INT64, name STRING);"
      engine:
        bigquery:
          location: "US"

Known Limitations

See docs/limitations.md for the complete list of known limitations and compatibility constraints.

2. Generate the Airflow DAG

Create a Python file named my_pipeline.py in your Airflow DAGs folder:

from orchestration_pipelines_lib.api import generate

# Generate Airflow DAG from pipeline definition file
# airflow | dag
# Root is "dags" directory in Composer bucket
generate("dataform-pipeline-local.yml")

Airflow will parse this Python file and automatically generate the DAG based on your YAML definition.

Advanced Features

Versioning and Manifests

You can manage multiple versions of your pipelines using a manifest.yml file. This allows you to specify which version of a pipeline should be active.

See the examples/ directory for a sample manifest.yml and how to use it.

Contributing

Contributions are welcome! Please see contributing.md for guidelines.

License

This project is licensed under the Apache 2.0 License - see the LICENSE file for details.