How to schedule data pipelines with Apache Airflow

· Category: Data Science

Short answer

Apache Airflow orchestrates data pipelines by representing workflows as directed acyclic graphs of tasks with scheduling, dependency management, and monitoring.

Steps

  1. Define a DAG file that specifies tasks, dependencies, and the execution schedule.
  2. Implement tasks using operators such as PythonOperator, BashOperator, or database transfers.
  3. Set retries and retry delays to handle transient failures gracefully.
  4. Configure backfill and catchup behavior to handle missed schedule intervals.
  5. Monitor execution through the Airflow web interface and trigger alerts on failures.

Tips

  • Keep DAG files idempotent so re-running produces consistent results.
  • Use task groups and sub-DAGs to organize complex workflows.
  • Store secrets and credentials in Airflow connections or external secret backends.
  • Test DAGs locally before deploying to production schedulers.

Common issues

  • DAG parsing errors due to top-level code executing during import.
  • Scheduler delays when DAG files grow too large or complex.
  • Task timeouts from long-running operations without proper resource allocation.
  • Dependency conflicts between Airflow Python version and task libraries.

Example

import pandas as pd
import numpy as np

df = pd.DataFrame({'sales': [100, 150, 200, np.nan]})
df['sales'] = df['sales'].fillna(df['sales'].median())
print(df.describe())

This snippet creates a DataFrame, handles a missing value with the median, and prints summary statistics common in exploratory analysis.