DAG Factory 1.0: Simplifying Airflow DAG Creation for Modern Data Teams

Data engineers manage hundreds of DAGs across their organizations. Each DAG represents critical business logic, from ETL pipelines to ML workflows. Yet creating and maintaining these DAGs at scale remains a challenge—teams struggle with code duplication, configuration drift, and the steep learning curve of teaching Python to every team member who needs to create data pipelines.

DAG Factory, the open-source tool for declarative DAG authoring in Apache Airflow, reaches a major milestone with version 1.0. This release delivers a modernized approach to DAG creation that aligns with Airflow 3 standards while maintaining the simplicity that makes DAG Factory accessible to data teams.

The Challenge: Scaling DAG Development

As data teams grow, they face mounting pressure to deliver more pipelines faster. Writing DAGs in Python can create bottlenecks. On teams where not everyone knows Python, maintaining consistency across hundreds of DAGs becomes difficult, and teams often build custom abstractions that are expensive to maintain.

Many organizations have built internal tools to abstract away Airflow's complexity. But these homegrown solutions require ongoing maintenance, lack community support, and create technical debt that slows down innovation.

What's New in DAG Factory 1.0

DAG Factory 1.0 represents a complete modernization of the project, positioning it as the standard for declarative DAG authoring in Airflow. Here's what's new:

Modernized YAML Specification

The YAML specification now aligns with modern Airflow patterns. The schedule parameter replaces the legacy schedule_interval, and start_date can be defined at the DAG level rather than buried in default_args. These changes make DAG configuration more intuitive and consistent with Airflow 3 conventions.

my_data_pipeline:
  schedule: "@daily"  # Modern scheduling syntax
  start_date: "2024-01-01"  # DAG-level configuration
  tasks:
    - task_id: extract_data
      operator: airflow.operators.bash_operator.BashOperator
      bash_command: "echo 'Extracting data'"
    - task_id: transform_data
      operator: airflow.operators.python_operator.PythonOperator
      python_callable_name: transform_function
      python_callable_file: /path/to/transforms.py
      dependencies: [extract_data]

Tasks and task groups are now defined as lists, making the configuration more readable and easier to reason about—especially for complex DAGs with dozens of tasks.

Intelligent Default Inheritance

DAG Factory 1.0 introduces a layered default system that eliminates repetitive configuration. Create a defaults.yml file at your project root, and every DAG inherits those settings. Need team-specific defaults? Add another defaults.yml in a subdirectory. The system intelligently merges configurations, with more specific settings overriding general ones.

# /dags/defaults.yml - Global defaults
default_args:
  owner: "data-team"
  retries: 2
  retry_delay: "5m"

# /dags/analytics/defaults.yml - Team-specific overrides
default_args:
  owner: "analytics-team"
  email: ["analytics@company.com"]

This approach standardizes configurations across your entire DAG portfolio while maintaining flexibility for team-specific requirements.

How Inheritance Works Across Folders

The inheritance system intelligently merges configurations from parent and child directories. When a DAG is in a subfolder, it inherits settings from the parent defaults.yml while allowing the subfolder's defaults.yml to override specific values and add new ones.

Here's how it works in practice:

# /dags/defaults.yml - Parent folder defaults
default_args:
  email: ["data-team@company.com"]  retries: 2
# /dags/pipeline_a.yml - Inherits parent defaults# This DAG automatically gets:#   email: ["data-team@company.com"]#   retries: 2

When you add a subfolder with its own defaults:

# /dags/analytics/defaults.yml - Subfolder overrides and additions
default_args:
  email: ["analytics@company.com"]  # Override parent's email
  retry_delay: "10m"                 # Add new parameter# /dags/analytics/pipeline_b.yml - Inherits merged configuration# This DAG automatically gets:#   email: ["analytics@company.com"]  # From subfolder (override)#   retry_delay: "10m"                # From subfolder (new)#   retries: 2                         # From parent (inherited)

The final configuration for pipeline_b.yml combines:

• Overridden values: The subfolder's email replaces the parent's
• New values: The subfolder's retry_delay is added
• Inherited values: The parent's retries is preserved

This layered approach ensures that:

• Organization-wide standards are maintained at the root level
• Teams can customize their specific needs without repeating common configurations
• Individual DAGs inherit the most specific applicable defaults
• Changes to defaults automatically propagate to all relevant DAGs

Simplified Entry Point

Previous versions required multiple entry points and methods for DAG generation. Version 1.0 consolidates everything into a single, clean interface using load_yaml_dags(). Airflow's native mechanisms handle DAG lifecycle management.

# /dags/generate_dags.py
from dagfactory import load_yaml_dags

# That's it - all your YAML DAGs are now loaded
load_yaml_dags(globals_dict=globals())

Full Airflow 3 Compatibility

DAG Factory 1.0 is built for the future. It fully supports Airflow 3's new features including improved scheduling options, asset-based orchestration, and the modernized UI. Your declarative DAGs automatically benefit from Airflow 3's performance improvements and new capabilities.

Enhanced Developer Experience

The new CLI provides powerful tools for validating and migrating DAGs:

# Validate your YAML configurations
dagfactory lint my_dag.yaml

# Convert Airflow 2 configurations to Airflow 3 format
dagfactory convert --input old_dag.yaml --output new_dag.yaml

Clear error messages and validation help catch issues before deployment, reducing debugging time and improving developer productivity.

Migration Path

Upgrading to DAG Factory 1.0 is straightforward for most users. While there are some breaking changes (detailed in the full release notes), the migration guide has a comprehensive ten-step process to migrate your existing DAG configurations to be compatible with v1.0. The primary changes involve updating parameter names to align with Airflow conventions and adjusting how Kubernetes configurations are specified.

Real-World Impact

DAG Factory simplifies how teams build and maintain data pipelines:

• Faster Onboarding: New team members can create DAGs without learning Python
• Consistent Standards: Enforced patterns through configuration reduce errors
• Reduced Maintenance: Less code means fewer bugs and easier updates
• Team Autonomy: Data analysts and domain experts can create pipelines independently

Organizations using DAG Factory report significant reductions in DAG development time and maintenance overhead. The declarative approach makes DAGs self-documenting, improving collaboration across teams.

Getting Started

Ready to simplify your DAG development? Here's how to get started:

1. Install DAG Factory: bash

pip install dag-factory==1.0.0

2. Follow the Quickstart Guide: The quickstart guide walks through creating your first declarative DAG in minutes.
3. Explore Examples: The examples directory contains templates for common patterns including data pipelines, ML workflows, and task dependencies.
4. Join the Community:
   • Star the GitHub repository to stay updated
   • Report issues or request features through GitHub Issues
   • Connect with other users in the Airflow Slack community