airflow-dag-patterns
Build production Apache Airflow DAGs with best practices for operators, sensors, testing, and deployment. Use when creating data pipelines, orchestrating workflows, or scheduling batch jobs.
Security Assessment
About airflow-dag-patterns
The 'airflow-dag-patterns' skill provides users with production-ready patterns for building Apache Airflow Directed Acyclic Graphs (DAGs) by adhering to best practices in design, operators, sensors, testing, and deployment. It addresses common challenges faced by data engineers and developers when orchestrating workflows, managing task dependencies, and ensuring the robustness of data pipelines. With this skill, users can streamline their workflow orchestration while maintaining high-quality standards in their DAG implementations.
FAQ
What are the main use cases for this skill?
This skill is designed for creating data pipeline orchestration, designing DAG structures, implementing custom operators and sensors, testing DAGs locally, setting up Airflow in production, and debugging failed DAG runs.
Is this skill compatible with all versions of Apache Airflow?
While the skill primarily focuses on features available in Airflow 2.0 and above, many principles and practices can still be applied to earlier versions.
What are the prerequisites for using this skill?
Users should have a basic understanding of Apache Airflow, Python programming, and familiarity with concepts like DAGs and task dependencies.
Can this skill be used for real-time data processing?
The skill is primarily aimed at batch job scheduling and workflow orchestration, thus, real-time processing scenarios may require additional configurations and setups.
What testing strategies are recommended with this skill?
Testing can be conducted locally with unit tests for individual tasks and integration tests for overall DAG execution to ensure reliable workflows.
Install airflow-dag-patterns
Quick Setup:
- Copy the skill folder to
.claude/skills/ - Claude will automatically detect and use the skill
Repository
wshobson/agents