Branching Conditionality is a vital characteristic of many DAGs
How usually is it that you simply’re writing a Information Pipeline and you then want you possibly can do one thing contingently? One thing that solely occurs if a set of situations are happy?
Hopefully, not that always! Airflow has supported this kind of performance through the AirflowBranchPython Operator. Many different workflow Orchestration instruments have adopted go well with. Prefect have Conditional Flows, Dagster have DyanmicOutput, and in Orchestra we facilitate branching based mostly on standing.
This leads us to a very powerful query: Why?
Why trouble in any respect with branching, thereby making your pipeline extra sophisticated than it must be. We’ll see there are literally some fairly unbelievable use-cases, particularly for people which are searching for a larger quantity of automation of their lives.
Earlier than diving in to use-cases, we’ll use the under code as a reference so we are able to perceive how branching works in follow.
from airflow import DAG
from airflow.operators.dummy import DummyOperator
from…