Apache Airflow is an open-source platform first created at Airbnb in 2014 by Maxime Beauchemin. It was later given to the Apache Software Foundation in 2016. Airflow helps teams build, schedule and monitor workflows in a clear way using Python. Over time it has become a leading tool in data engineering and cloud based automation. Many roles such as data engineers, data scientists and cloud architects work with it every day. To prepare for these jobs, let us look at the top Airflow interview questions and answers.
Fun Fact: Apache Airflow’s logo is designed to look like a pinwheel, symbolizing the flow of tasks and pipelines in motion.
Airflow Interview Questions for Freshers
Here are some commonly asked Apache Airflow interview questions and answers to help freshers prepare.
- What is Apache Airflow and why is it used?
Apache Airflow is an open-source workflow orchestration tool created at Airbnb. It was later donated to the Apache Software Foundation. I use it to schedule, run, and monitor data pipelines. Teams prefer it because workflows can be written in Python, making them flexible and easy to maintain.
- What is a DAG in Airflow?
A DAG stands for Directed Acyclic Graph. It is the core concept in Airflow. A DAG represents a pipeline where tasks are defined as nodes and their order as edges. Since it’s acyclic, tasks never loop back, avoiding infinite runs.
- What are the main components of Airflow’s architecture?
Airflow has four major components.
- The Scheduler schedules and triggers tasks
- Executors run those tasks
- The Metadata Database stores task states, DAG runs, variables, and connection details
- The Webserver provides the UI for monitoring and management
- How does the Airflow Scheduler work?
The Scheduler continuously checks DAG definitions. It identifies tasks that are ready and sends them to the Executor. It works with cron expressions or custom intervals to run pipelines on time.
- What are Operators in Airflow? Can you give some examples?
Operators define the type of work a task does. Common examples are PythonOperator for Python functions, BashOperator for shell commands, and EmailOperator for sending emails. Many providers also ship operators for systems like AWS, GCP, and Databricks.
- What are Hooks in Airflow and how are they used?
Hooks are interfaces that connect Airflow to external systems. They handle the low-level code for things like connecting to MySQL, S3, or APIs. For example, S3Hook allows reading and writing files in Amazon S3.
- What is the role of Airflow Variables?
Variables store small key-value pairs that DAGs can use at runtime. They are useful for dynamic configurations, like table names, environment flags, or dates, without hardcoding them in DAG files.
- How do you define task dependencies in Airflow?
Dependencies are set with bit-shift operators (>> or <<) or with methods like set_upstream and set_downstream. For example, task1 >> task2 means task2 runs after task1 finishes.
Also Read - Top 75+ Python Interview Questions and Answers
Apache Airflow Interview Questions for Experienced Professionals
Let’s go through some advanced interview questions on Airflow that are often asked to experienced professionals in data engineering roles.
- Explain the difference between LocalExecutor, CeleryExecutor, and KubernetesExecutor.
LocalExecutor runs tasks in parallel on the same machine. It’s good for small setups.
CeleryExecutor distributes tasks across multiple workers using Celery, making it suitable for medium to large teams.
KubernetesExecutor runs each task in its own Kubernetes Pod, giving the best scalability and isolation for production environments.
- What are XComs in Airflow? How are they typically used?
XComs, short for cross-communications, let tasks share small pieces of data. They store values in the metadata database with keys. For example, one task can push a filename to XComs, and another task can pull it later. They are not meant for big data transfers, only for metadata or flags.
- What is the TaskFlow API and why is it useful?
The TaskFlow API lets you write DAGs in a more Pythonic way. Instead of manually pushing and pulling with XComs, you can simply return values from one task and pass them as arguments to another. This reduces boilerplate code and makes DAGs easier to read.
- How do you monitor and debug failed DAG runs in production?
I usually start with the Airflow UI grid view. Failed tasks are marked red. Clicking on them gives logs that show the stack trace or error message. If retries don’t solve it, I check system-level issues like permissions, missing connections, or resource limits. For critical jobs, I also set alerts through callbacks or integrations like PagerDuty or Slack.
- What is the role of ExternalTaskSensor?
ExternalTaskSensor waits for a task in another DAG to finish before running the current task. It’s useful when multiple DAGs depend on each other, such as one DAG loading raw data and another processing it afterward.
- What are some best practices for writing production-ready DAGs?
Keep tasks idempotent so re-runs don’t break data. Avoid hardcoding credentials and use Connections or secret managers instead. Add retries with delays for unstable sources. Break large pipelines into smaller DAGs for easier maintenance. Always test DAGs locally or in staging before moving to production.
Apache Airflow MCQs
Here are a few multiple-choice Airflow interview questions to test your knowledge and quick thinking.
- In Airflow, what does DAG stand for?
a) Directed Acyclic Graph
b) Data Aggregation Graph
c) Dynamic Application Graph
d) Distributed Application Group
Answer: Directed Acyclic Graph
- Which Airflow component is responsible for triggering tasks?
a) Webserver
b) Scheduler
c) Executor
d) Worker
Answer: Scheduler
- By default, what executor is used in Airflow after installation?
a) CeleryExecutor
b) KubernetesExecutor
c) SequentialExecutor
d) LocalExecutor
Answer: SequentialExecutor
- Which operator is used to run Python functions in Airflow?
a) BashOperator
b) PythonOperator
c) BranchPythonOperator
d) TriggerDagRunOperator
Answer: PythonOperator
- What is stored in Airflow’s Metadata Database?
a) Only DAG code
b) Task logs
c) DAG runs, task states, variables, and connections
d) Scheduling intervals
Answer: DAG runs, task states, variables, and connections
- What is the default webserver port for Apache Airflow?
a) 7070
b) 8080
c) 9090
d) 5000
Answer: 8080
Tips to Prepare for Airflow Interview
Preparing for an Airflow interview requires strong fundamentals and clear understanding of workflows and DAGs.
- Revise Airflow basics like DAGs, tasks, scheduler, and executors
- Practice writing sample DAGs using PythonOperator and BashOperator
- Understand XComs, Variables, and TaskFlow API with real examples
- Learn common executors: Local, Celery, Kubernetes
- Review monitoring, debugging, and retry strategies in production
- Be ready with scenario-based answers like handling DAG failures or external dependencies
Also Read - Top 75+ Python Interview Questions and Answers
Wrapping Up
With these 20 Airflow interview questions and answers, you now have a solid base to prepare with confidence. Focus on both theory and practical scenarios, and practice writing DAGs to stand out in interviews.
Ready to take the next step? Find top IT opportunities, including Airflow jobs on Hirist.
FAQs
Airflow is used to schedule, manage, and monitor workflows. It helps data teams build pipelines for ETL, machine learning, and other automation tasks in a reliable way.
The concept of Airflow is simple: workflows are defined as Directed Acyclic Graphs (DAGs). Each DAG contains tasks with dependencies, and Airflow runs them in the right order at the right time.
Yes. Airflow is widely used for ETL because it lets you orchestrate extract, transform, and load pipelines, schedule them flexibly, and monitor their progress.
Interviewers often ask how you would design a DAG for a real ETL pipeline, handle backfilling for missed runs, manage dependencies across multiple DAGs, or deal with task retries and failures. They may also ask how you would set up monitoring and alerts in production or scale Airflow for large data workloads.
Leading firms like Airbnb, Amazon, Google, Microsoft, Databricks, and Deloitte hire professionals skilled in Airflow, along with many startups and mid-size data-driven companies.
