Airflow Competency Test: Master the Essentials of Workflow Orchestration

Learning Objectives

Demonstrate Pipeline Orchestration Skills

Identify common issues in DAG execution, such as failed tasks, incorrect scheduling, and broken dependencies.

Leverage advanced Airflow features like task retries, alerts, backfills, and catch-up mechanisms to ensure robustness in pipelines.

Use SubDAGs, Task Groups, and XComs to simplify complex workflows and facilitate task communication.

Understand and Apply Airflow Scheduling

Optimize and Modularize Complex Workflows

Overview

Purpose:
The mock test is designed to assess your comprehension and practical application of Apache Airflow's core concepts, architecture, and components. It aims to evaluate your ability to utilize Airflow effectively in managing ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) pipelines.

Test Structure:
The test consists of scenario-based questions that mirror real-world challenges you might face when working with Airflow. The focus is on practical application rather than just theoretical knowledge, ensuring that you can implement and troubleshoot Airflow in various situations.

Topics Covered:

Core Concepts and Architecture:
- Questions in this section will test your understanding of Airflow's fundamental building blocks, such as DAGs (Directed Acyclic Graphs), tasks, operators, and sensors.
- You will need to explain how different components like the Scheduler, Executor, and Web Server interact within the Airflow ecosystem.
ETL/ELT Pipeline Management:
- This section includes scenario-based questions that require you to design, configure, and manage ETL/ELT pipelines using Airflow.
- You may need to choose the appropriate operators, set dependencies, and ensure efficient task execution.
Practical Application and Real-World Scenarios:
- Here, the questions will be more challenging, pushing you to apply Airflow in realistic use cases, such as handling data workflows, scheduling, and monitoring.
- You might be asked to optimize pipeline performance or manage complex dependencies and retries.
Debugging and Troubleshooting:
- This section focuses on identifying and fixing common issues in Airflow pipelines.
- You will encounter debugging tasks where you need to analyze logs, identify errors, and suggest fixes or improvements.
Code Completion and Best Practices:
- In this part, you will work with partially written code snippets where you need to complete Airflow DAGs or task definitions.
- Emphasis will be on best practices, such as error handling, using XComs for inter-task communication, and ensuring the maintainability of DAGs.

This mock test is crafted to not only test your theoretical knowledge but also to gauge your practical skills in working with Apache Airflow in various real-world data workflow scenarios.

Prerequisites

Basic to intermediate knowledge of Python.
Understanding of ETL/ELT processes.
Familiarity with pipeline orchestration and task scheduling.
Understanding of Airflow's scheduling mechanism, task execution, retries, and catch-up behavior.
Understanding of `Xcoms`, `Connections`, `Hooks`, and `Variables`
Knowledge of Jinja templating in Airflow for dynamically setting parameters
Knowledge of SubDags, and TaskGroups in breaking down large, complex DAGs into smaller sub-DAGs