DAG versioning strategies in Apache Airflow - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is a DAG in Airflow?

A DAG (Directed Acyclic Graph) in Airflow is a collection of tasks organized in a way that reflects their relationships and dependencies, defining the order in which tasks run.

beginner

Why is versioning DAGs important in Airflow?

Versioning DAGs helps track changes, avoid conflicts, and safely deploy updates without breaking running workflows.

beginner

Name one common strategy for DAG versioning.

Using Git branches or tags to manage different versions of DAG code is a common strategy.

intermediate

How can you keep multiple versions of a DAG running simultaneously?

By including version numbers in the DAG ID, you can deploy multiple DAG versions side by side in Airflow.

intermediate

What is a risk of not versioning DAGs properly?

Without proper versioning, changes can break workflows, cause data loss, or make it hard to track what code is running.

What does DAG stand for in Airflow?

A Directed Acyclic Graph

B Data Analysis Group

C Dynamic Application Gateway

D Distributed Async Generator

DAG stands for Directed Acyclic Graph, which is a structure that defines task dependencies in Airflow.

Which method helps run multiple DAG versions simultaneously?

A Deleting old DAGs before deploying new ones

B Using different DAG IDs with version numbers

C Running DAGs on separate Airflow servers

D Changing task names inside the DAG

Including version numbers in DAG IDs allows multiple versions to coexist in Airflow.

What tool is commonly used to manage DAG code versions?

A Jenkins

B Docker

C Kubernetes

D Git

Git is widely used to track and manage changes in DAG code.

What is a key benefit of DAG versioning?

A Safe deployment of updates

B Faster task execution

C Automatic error fixing

D Unlimited task retries

Versioning helps deploy updates safely without breaking existing workflows.

What happens if you don’t version DAGs properly?

A Tasks will run faster

B Airflow will automatically fix errors

C Workflows may break or behave unexpectedly

D DAGs will run in parallel

Improper versioning can cause workflow failures and unpredictable behavior.

Explain why and how you would implement DAG versioning in Airflow.

Think about how software versioning helps manage changes safely.

Describe a strategy to run multiple versions of the same DAG without conflicts.

Consider how naming helps Airflow distinguish DAGs.