Apache Airflow Interview Questions & Answers (Story-Based)

Imagine you have joined a company as a Data Engineer. Every day, thousands of files arrive from different systems. Your job is to automate the entire data pipeline—from receiving files to generating business reports.

This is exactly where Apache Airflow comes into the picture.

Let's walk through some of the most common interview questions using real-world scenarios.

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1. What is Apache Airflow?

Imagine a company receives sales data every night at 12:00 AM.

The company needs to:

• Download the file
• Clean the data
• Load it into a database
• Generate reports
• Send an email to management

Doing all of this manually every day would be time-consuming and error-prone.

This is where Apache Airflow helps.

Apache Airflow is an open-source workflow orchestration platform used to schedule, automate, and monitor workflows.

Instead of running scripts manually, Airflow executes them automatically according to a schedule or event.

Real-World Example

Every night:

Download Sales File
        ↓
Validate Data
        ↓
Transform Data
        ↓
Load into Database
        ↓
Generate Dashboard
        ↓
Send Email Report

Airflow manages the complete workflow automatically.

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2. Why do we need Apache Airflow?

Suppose your company has 50 different ETL jobs.

Some jobs depend on others.

For example,

Extract Data
      ↓
Clean Data
      ↓
Transform Data
      ↓
Load Warehouse
      ↓
Create Dashboard

If Clean Data fails,

should Load Warehouse still run?

Of course not.

Airflow understands task dependencies and executes tasks in the correct order.

Benefits

• Automates repetitive workflows
• Handles task dependencies
• Schedules jobs automatically
• Retries failed tasks
• Sends alerts on failures
• Monitors workflow execution
• Easy to scale

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3. What is a DAG in Apache Airflow?

A DAG stands for Directed Acyclic Graph.

It represents the complete workflow in Airflow.

Think of a DAG as a project plan.

Example:

Receive Orders
       ↓
Validate Orders
       ↓
Calculate Revenue
       ↓
Store Results
       ↓
Notify Users

Each box is a task, and together they form a DAG.

Why is it called Directed Acyclic?

• Directed → Tasks follow a direction.
• Acyclic → Tasks cannot create loops.
• Graph → Multiple tasks can connect together.

For example,

✅ Valid

A → B → C

❌ Invalid

A → B → C
↑       ↓
← ← ← ←

Airflow does not allow circular dependencies.

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4. Explain the Apache Airflow Architecture.

Apache Airflow consists of several components working together.

Component	Responsibility
Scheduler	Decides when tasks should run
Webserver	Provides the Airflow UI
Executor	Sends tasks for execution
Worker	Executes the tasks
Metadata Database	Stores DAG runs, task states, users, and configurations

Workflow

          Scheduler
               │
               ▼
          Executor
               │
               ▼
            Worker
               │
               ▼
     Metadata Database
               ▲
               │
          Webserver

Interview Tip

The Scheduler is often called the "brain" of Airflow because it determines when tasks are ready to execute.

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5. What is the role of the Scheduler in Apache Airflow?

The Scheduler continuously checks:

• Which DAGs are available?
• Which tasks are ready?
• Which dependencies are completed?
• Which schedules have been reached?

Once everything is satisfied, it sends the task to the Executor.

Real-World Story

Imagine an airport.

Passengers cannot board until:

• Flight is available
• Gate is open
• Crew is ready
• Security check is complete

Similarly, the Scheduler waits until all task dependencies are satisfied before allowing execution.

Example

Suppose your DAG contains:

Download File
      ↓
Clean Data
      ↓
Load Database

The Scheduler ensures:

• Download finishes first.
• Then Clean Data starts.
• Finally Load Database executes.

The tasks never run in the wrong order.

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Summary

Question	Topic
1	What is Apache Airflow?
2	Why do we need Airflow?
3	What is a DAG?
4	Airflow Architecture
5	Scheduler Explained

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Interview Tip

A common beginner interview question is:

"What is the difference between Apache Airflow and Apache Spark?"

A simple answer is:

• Apache Airflow is used to orchestrate and schedule workflows.
• Apache Spark is used to process large amounts of data quickly.

In many real-world projects, Airflow schedules and manages Spark jobs, making them complementary technologies rather than competitors.