How Can Airflow’s Branch Operator Solve Your Workflow Branching Problems?

3 min readSep 18


The concept of Branching Tasks in Airflow Source:123RF

Apache Airflow is a popular open-source platform used for orchestrating complex workflows. One of its most powerful features is the ability to handle branching within Directed Acyclic Graphs (DAGs). Branching allows you to create conditional workflows, enabling different paths to be taken based on the outcomes of previous tasks. In this article, we will delve into the concept of branching in Airflow and demonstrate its use with the Branch Operator in a practical scenario.

Scenario Overview

Imagine you have a DAG that is responsible for extracting data from a source, cleaning and transforming it, and then loading it into a database. This is a common ETL (Extract, Transform, Load) process used in data pipelines. However, your source system has recently changed, and you now need to add a new branch to your DAG for data extraction from the new source. You want to decide which branch to take based on certain conditions, such as the availability of the new source data.

Branching in Airflow

Branching in Airflow allows you to define multiple paths in your DAG based on the results of a previous task. This is achieved using the BranchPythonOperator, a specialized operator that runs a Python function to determine which downstream tasks to execute. Here’s how it works:

  1. You create a BranchPythonOperator task and provide it with a Python callable (a function) that returns the task_id of the next task to execute based on certain conditions.
  2. The BranchPythonOperator executes its Python callable and returns the task_id of the next task to execute.
  3. Airflow uses this task_id to decide which path to take in the DAG.
from airflow import DAG
from airflow.operators.python_operator import BranchPythonOperator
from airflow.operators.dummy_operator import DummyOperator
from datetime import datetime

def decide_branch(**context):
# Logic to determine which branch to take
if context["execution_date"] >'2023-09-15':
return 'extract_from_new_source'
return 'extract_from_old_source'

# Create a DAG
dag = DAG('etl_with_branching', start_date=datetime(2023, 9, 16))

# Start with the initial task
start_task = DummyOperator(task_id='start_task', dag=dag)

# Define the BranchPythonOperator
branch_task = BranchPythonOperator(

# Define the two branches
extract_from_new_source_task = DummyOperator(task_id='extract_from_new_source', dag=dag)
extract_from_old_source_task = DummyOperator(task_id='extract_from_old_source', dag=dag)

# End task
end_task = DummyOperator(task_id='end_task', dag=dag)

# Define the workflow
start_task >> branch_task
branch_task >> [extract_from_new_source_task, extract_from_old_source_task]
[extract_from_new_source_task, extract_from_old_source_task] >> end_task
Branching In Airflow Dags

In this example:

  1. decide_branch is a Python function that contains the logic to determine which branch to take based on a condition. It returns the task_id of the next task to execute.
  2. The BranchPythonOperator, branch_task, is used to execute the decide_branch function and decide which branch to follow.
  3. Depending on the condition specified in decide_branch, the DAG will either follow the path to extract_from_new_source_task or extract_from_old_source_task.

Branching in Apache Airflow using the BranchPythonOperator allows you to create flexible and dynamic workflows that adapt to changing conditions. In our scenario, we demonstrated how to use branching to decide which branch of a DAG to execute based on a condition. This is a powerful feature for handling real-world scenarios where decision-making is a critical part of your workflow, such as ETL processes, data validation, and more. By harnessing the Branch Operator’s capabilities, you can design more robust and adaptive workflows in Airflow.

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Data Engineer, Big Data Enthusiast and Automation using Python