Telecom Data Management with Airflow Task Groups

Learning Objectives

Understand the need for task groups in managing complex Airflow DAGs.

Implement task groups in Airflow to organize related tasks.

Refactor existing DAGs to improve structure and maintainability using task groups.

Evaluate different approaches for task grouping based on industry best practices.

Gain hands-on experience in creating modular and scalable workflows in Airflow.

Overview

WeTelco, the telecom company deals with massive amounts of data generated daily, including customer information, billing records, device details, and various other operational data. The company needs to process this data efficiently to create backups, perform aggregations, and derive insights for better business decisions.

Initially, the company's Airflow DAG was relatively simple. However, as the business expanded, the complexity of the DAG increased. Multiple tasks, such as fetching data from PostgreSQL, creating backups in S3, and performing various analyses on the data, were introduced. The growing complexity made the DAG difficult to manage, particularly when tasks increased or required different execution patterns.

To address this challenge, the company decided to adopt the Task Group approach in Airflow. By grouping related tasks together, they aimed to simplify the DAG structure, making it more maintainable and scalable. Specifically, they created a task group for all incremental backup tasks, while different aggregated views, which are related to different analyses, remained independent.

Prerequisites

Basic knowledge of Python programming.
Familiarity with Airflow concepts, including DAGs, tasks, and dependencies.
Understanding of cloud storage (e.g., AWS S3) and databases (e.g., PostgreSQL).
Experience with basic ETL processes.