Top Airflow Interview Questions & Answers

1. Introduction

Preparing for an interview involving Apache Airflow can be a daunting task, given its intricate architecture and extensive usage in workflow management. This article aims to provide a comprehensive guide to some of the most common and insightful airflow interview questions. These questions are designed to gauge not only your technical knowledge but also your practical experience with the platform.

2. Understanding Apache Airflow and Its Ecosystem

Apache Airflow is an open-source platform used to design, schedule, and monitor workflows. As workflow management has become a critical component of data engineering, the role of Airflow has grown significantly in orchestrating complex data pipelines. Proficiency in Airflow is highly sought after in roles demanding robust data processing and scheduling capabilities. The questions you may encounter in an interview are often a mix of conceptual, technical, and practical inquiries that assess your end-to-end understanding of the platform. A candidate who can demonstrate a deep comprehension of Airflow’s components, its operational dynamics, and how it integrates with other tools will stand out in the hiring process.

3. Airflow Interview Questions

Q1. Can you explain what Apache Airflow is and why it is used? (Conceptual Understanding)

Apache Airflow is an open-source platform designed for scheduling and monitoring workflows. It is used to programmatically author, schedule, and monitor complex workflows. Airflow uses directed acyclic graphs (DAGs) to define the sequence of tasks and their dependencies. It is used because:

• Scalability: Airflow can scale to handle a large number of tasks and is flexible in terms of integrating with various execution environments like local, remote, or cloud.
• Extensibility: It provides a rich set of operators to handle various types of tasks and can be extended to define custom operators.
• Dynamic: DAGs are written in Python, which makes them dynamic and capable of generating workflows programmatically.
• User Interface: Airflow comes with a web-based UI that makes it easy to visualize pipelines running in production, monitor progress, and troubleshoot issues.
• Community: Being an Apache project, it has a strong community which constantly contributes to its development and support.

Q2. Why do you want to work with Apache Airflow? (Candidate Motivation)

How to Answer
Discuss your motivation for wanting to work with Airflow, highlighting specific features or experiences that make it appealing to you. You could touch on aspects like the Python-based framework, its rich set of integrations, or the active community.

My Answer
I want to work with Apache Airflow because it empowers me to define complex workflows as code, which I find to be a highly transparent and powerful approach to workflow management. Its Python-based scripting for defining DAGs aligns well with my programming skills and makes it versatile for integration with various systems. Moreover, the active community and constant updates mean that I can count on Airflow to be at the cutting edge of workflow automation technology.

Q3. How does Airflow differ from other workflow management systems you have used? (Comparative Knowledge)

Apache Airflow differs from other workflow management systems in several ways:

• DAGs: Unlike some systems that use XML or other configuration files, Airflow uses Python scripts for DAG definition, offering more flexibility and power.
• Extensibility: Airflow’s plugin architecture allows users to write custom operators, sensors, and hooks, making it highly customizable.
• UI: Airflow’s web-based UI is more comprehensive in terms of viewing DAG dependencies, task logs, and retrying failed tasks.
• Scheduler: Airflow’s scheduler is lean and allows for backfilling jobs as well as running jobs in parallel, offering more efficient job management.
• Community: Airflow has a larger and more active community compared to many other open-source workflow systems.

Q4. Can you describe the core components of Airflow’s architecture? (Technical Knowledge)

The core components of Airflow’s architecture include:

• Web Server: A Flask-based web application that provides the user interface to view and manage DAGs.
• Scheduler: The heart of Airflow. It schedules tasks on DAGs, following the defined dependencies.
• Metadata Database: Stores the state of all tasks and DAGs, and supports the scheduler and web server with necessary data.
• Executor: Responsible for running the tasks. Airflow supports several executors, such as LocalExecutor, SequentialExecutor, CeleryExecutor, and KubernetesExecutor.
• Worker: The process that actually executes the logic of tasks when the CeleryExecutor or KubernetesExecutor is used.
• Message Broker: Used with CeleryExecutor to queue tasks. Common message brokers include RabbitMQ and Redis.

Q5. How do you define a workflow in Airflow? (Practical Application)

A workflow in Airflow is defined as a Directed Acyclic Graph (DAG). To define a workflow, you would:

• Import the required modules and classes.
• Define the default arguments for the DAG (e.g., start date, email on failure).
• Instantiate the DAG object with a unique name and the default arguments.
• Define tasks using operators and set the task dependencies to outline the workflow structure.

Here’s an example of defining a simple DAG with two tasks:

from datetime import datetime
from airflow import DAG
from airflow.operators.dummy_operator import DummyOperator

# Default arguments for the DAG
default_args = {
    'owner': 'airflow',
    'start_date': datetime(2023, 1, 1),
    'email_on_failure': False
}

# Define the DAG
dag = DAG('example_dag',
          default_args=default_args,
          description='An example DAG',
          schedule_interval='0 * * * *')

# Define tasks
start_task = DummyOperator(
    task_id='start',
    dag=dag
)

end_task = DummyOperator(
    task_id='end',
    dag=dag
)

# Set dependencies
start_task >> end_task

In this example, we define a simple DAG with two tasks, start_task and end_task. The DummyOperator is used here for simplicity, which doesn’t perform any action. The start_task is set to run before the end_task using the >> operator, which represents the direction of the dependency.

Q6. What are Operators in Airflow and can you name a few commonly used ones? (Airflow Specifics)

Operators in Airflow are the building blocks for workflows. They are the tasks that need to be executed, and they define what actually gets done in a DAG. Each operator performs a single, idempotent task.

Commonly used Airflow Operators include:

• BashOperator: Executes a bash command.
• PythonOperator: Executes a Python function or code.
• DAG: Defines a group of tasks with a directed acyclic graph.
• DAG: Defines a group of tasks with a directed acyclic graph.
• SimpleHttpOperator: Sends an HTTP request and processes the response.
• EmailOperator: Sends an email.
• MySqlOperator, PostgresOperator, SqliteOperator, MsSqlOperator: Executes a SQL command.
• Sensor: Waits for a certain condition to be true.

Q7. What is a DAG and how do you manage dependencies within it? (Workflow Management)

A DAG, or Directed Acyclic Graph, is a collection of all the tasks you want to run, organized in a way that reflects their relationships and dependencies. A DAG is defined in a Python script, which represents the DAGs structure (tasks and their dependencies) as code.

To manage dependencies within a DAG, you can set relationships between tasks using the set_downstream() and set_upstream() methods, or the bitshift operators >> and <<:

task1 >> task2 # task1 must be completed before task2
task1 << task2 # task2 must be completed before task1

Alternatively, you can use the depends_on_past argument to make a task dependent on the success of the same task in the previous run of the DAG.

Q8. Explain how you would secure sensitive information in Airflow? (Security)

To secure sensitive information in Airflow, you should:

• Use Airflow’s built-in Variables to store secrets and retrieve them in your DAGs as needed. However, since they are stored as plaintext in the metadata database, consider additional encryption.
• Set up Connections for external services with login and password fields encrypted in the metadata database.
• Enable Fernet encryption to encrypt passwords in the metadata database. Store the Fernet key in a secure location.
• Manage Airflow credentials with a secrets backend like HashiCorp Vault, AWS Secrets Manager, or GCP Secrets Manager.
• Use environment variables to store sensitive information outside of your code.
• Set up a role-based access control (RBAC) to restrict access to DAGs and tasks based on user roles.

Q9. How do you monitor and troubleshoot failed tasks in Airflow? (Monitoring & Troubleshooting)

To monitor and troubleshoot failed tasks in Airflow:

• Use the Airflow Web UI to monitor tasks and DAGs. It provides a detailed view of task history, logs, and can be used to retry tasks or mark them as successful.
• Set up email alerts or other notifications to be sent on task failure, retry, or other task lifecycle events.
• Inspect the logs for each task. The logs can provide insights into why a task failed.
• Use Airflow’s CLI to interact with and troubleshoot tasks and DAGs from the command line.
• Implementing custom metrics and monitoring with external systems like Prometheus or StatsD can provide more in-depth monitoring.

Q10. What are Hooks and what are they used for in Airflow? (Data Integration)

Hooks are interfaces to external platforms and databases. They act as building blocks for Operators, providing a reusable way to interact with external systems.

Hooks are used in Airflow to:

• Abstract the connection details of external systems.
• Provide a consistent interface for Operators to use external systems.
• Help in sharing connections across tasks, leading to better resource management.

Some common Airflow Hooks include:

• HttpHook: For making HTTP requests.
• PostgresHook: For interacting with PostgreSQL databases.
• S3Hook: For connecting to AWS S3.
• BigQueryHook: For Google BigQuery interactions.
• SqlHook: A base class for all SQL-related hooks.

Q11. Can you explain what XComs are, and how they are used in Airflow? (Inter-Task Communication)

XComs, short for "Cross-Communication," are a feature in Airflow that allows tasks to exchange messages or share small amounts of data between them. XComs enable tasks within the same DAG to push and pull data to one another, facilitating inter-task communication. This is particularly useful when the output of one task needs to be used as an input for another.

Data passed through XComs is stored in the Airflow metadata database, and it can be accessed by other tasks. The primary methods for using XComs are xcom_push() for sending data and xcom_pull() for retrieving data.

Example code snippet showing how to use XComs:

from airflow.models import XCom

def push_function(**kwargs):
    # Pushing data to XCom
    kwargs['ti'].xcom_push(key='sample_key', value='sample_data')

def pull_function(**kwargs):
    # Pulling data from XCom
    ti = kwargs['ti']
    pulled_value = ti.xcom_pull(key='sample_key')
    # Use the pulled_value for further processing

Q12. How would you scale Airflow to handle a large number of tasks? (Scalability)

To scale Airflow to handle a large number of tasks, several strategies can be applied:

• Increase the number of worker nodes: By adding more workers to the Airflow setup, you can distribute the execution of tasks across multiple machines, allowing the system to handle a greater load.
• Use a more powerful executor: Switching from the SequentialExecutor or LocalExecutor to the CeleryExecutor, KubernetesExecutor, or CeleryKubernetesExecutor can provide better scalability.
• Optimize DAG design: Ensure that the DAGs are designed to maximize parallelism and minimize inter-task dependencies. Use task groups and dynamic task generation where appropriate.
• Partitioning of data and workloads: If tasks can be split into smaller, independent units of work, it can lead to more granular parallel execution.
• Resource optimization: Tune the resources allocated to the worker nodes based on the task requirements. Use resource quotas and limits appropriately, especially in a containerized environment like Kubernetes.
• Monitoring and autoscaling: Implement monitoring to keep track of the system’s performance and set up autoscaling policies to adjust the number of workers dynamically based on the workload.

Q13. Describe the process of version controlling Airflow DAGs. (Version Control)

Version controlling Airflow DAGs is essential for collaboration, tracking changes, and maintaining the history of workflow definitions. The process typically involves the following:

1. Use a Version Control System (VCS): Store your DAGs in a VCS such as Git. This allows you to track changes, revert to previous versions, and collaborate with others.
2. Branching Strategy: Define a branching strategy for developing new DAGs or features. Common strategies include feature branching, git-flow, or trunk-based development.
3. Automated Testing: Set up CI/CD pipelines to automatically test your DAGs for syntax errors and other issues upon pushing changes to the repository.
4. Continuous Deployment: Automatically deploy the DAGs to your Airflow environment from the VCS. This can be done through automation tools such as Jenkins, GitLab CI/CD, or GitHub Actions.
5. DAG Versioning: Include a version number in the DAG file, either as a suffix to the DAG ID or within the DAG definition, to track the version of the DAG that is running.

Q14. How do you ensure that a task in Airflow is idempotent? (Data Processing Standards)

Ensuring that a task in Airflow is idempotent means that no matter how many times you execute the task, given the same input, it should produce the same output without causing unintended side effects.

• Designing Idempotent Tasks: Make sure that the logic within the task is idempotent. This could mean checking for the existence of files before writing or using database transactions.
• Use Airflow Features: Employ Airflow features such as task retries, retry delays, and catchup by depending on task state rather than external factors.
• External Systems: Coordinate with external systems to handle idempotence. For example, use unique identifiers or timestamps when inserting data into a database to prevent duplicates.
• Testing: Write tests for your tasks to ensure they behave idempotently and handle edge cases properly.

Q15. What is the role of the Scheduler in Airflow? (Airflow Components)

The Scheduler in Airflow is a core component responsible for several critical functions:

• Triggering Tasks: The Scheduler decides when to execute tasks based on the DAG definitions and their schedules.
• Scheduling Decisions: It uses the metadata database to determine the state of DAG runs and tasks and to make decisions about what should run next.
• Queuing Tasks: Once it decides that a task should run, the Scheduler places tasks in the queue, from where they are picked up by the available Executors.
• Handling Task Lifecycle: The Scheduler transitions tasks through different states, such as queued, running, success, or failure.

How the Scheduler Works:

Scheduler Action	Description
Parsing DAG files	Reads and parses DAG files to schedule and execute tasks.
Checking schedules	Evaluates the scheduling intervals and execution dates for DAGs.
Triggering DAG runs	Creates new DAG run entries in the metadata database.
Queuing tasks	Sends tasks to the message queue for the executor to run.
Monitoring tasks	Monitors running tasks and updates their state in the database.

Q16. How can you implement data quality checks in an Airflow pipeline? (Data Quality Assurance)

Data quality is a critical aspect of any data pipeline, and ensuring high quality of data is a common requirement in ETL processes. In an Airflow pipeline, you can implement data quality checks using a combination of built-in operators and custom logic.

How to Answer:
Discuss the various methods and operators that can be used to implement data quality checks in an Airflow pipeline. Highlight the importance of such checks and the implications of poor data quality. If applicable, mention any specific operators you have used and describe how you configured them.

My Answer:

• Using Sensors: Sensors are special kinds of operators in Airflow that wait for a certain condition to be true. You can use sensors to ensure that your data is available and meets certain conditions before proceeding with the pipeline.
• Custom Operators: You can create custom operators that perform specific data quality checks, such as verifying row counts, checking for null values, or ensuring data consistency across tables.
• Check Operators: Airflow provides operators like SqlSensor, CheckOperator, and ValueCheckOperator that can be used for performing checks against a database. You can use these to run SQL queries that validate your data, for example, checking if the number of rows in a table is within an expected range.
• Data Profiling: Using operators like PythonOperator, you can perform data profiling tasks which can include statistical analysis of the data to spot any anomalies or unexpected patterns.

Here’s an example of using a PythonOperator to perform a simple data quality check:

from airflow import DAG
from airflow.operators.python_operator import PythonOperator
from datetime import datetime

def check_data_quality(**kwargs):
    # Imagine we have a function that returns the row count
    row_count = get_row_count_from_table('my_table')
    if row_count < 1:
        raise ValueError('Data quality check failed: my_table is empty')
    print('Data quality check passed: my_table has rows')

dag = DAG('data_quality_example', default_args=default_args, schedule_interval='@daily')

quality_check = PythonOperator(
    task_id='quality_check',
    python_callable=check_data_quality,
    dag=dag,
)

# Assume other tasks are defined here

quality_check

Q17. Explain how you would use Airflow Variables and Connections. (Configuration Management)

Airflow offers Variables and Connections as two essential features for configuration management, allowing you to handle dynamic values and external systems configurations separately from your DAGs and tasks.

How to Answer:
Describe what Airflow Variables and Connections are and provide examples of how they can be used in a DAG. Explain how separating configuration from code can improve maintainability and security.

My Answer:

• Variables: Airflow Variables are a way to store and retrieve arbitrary content or settings as a simple key-value store within Airflow. This can be used to manage dynamic values that are used by your DAGs and tasks.
  • Examples include setting thresholds for data quality checks, managing environment-specific settings (such as file paths or API endpoints), and storing values needed for conditional logic in tasks.

Here’s a simple usage of Airflow Variables in a DAG:

from airflow.models import Variable

# Retrieve variable
threshold = Variable.get("row_count_threshold")

# Use the variable in a function or operator

• Connections: Airflow Connections are configurations that store the connection parameters for external systems. These are used by various operators and hooks to connect to databases, APIs, and other data sources or services.
  • You might use Connections to manage credentials for databases like PostgreSQL or MySQL, or to configure API keys and endpoints for third-party services.

To use a Connection in an Airflow task:

from airflow.hooks.base_hook import BaseHook

# Get connection information
conn_id = 'my_postgres_conn'
connection = BaseHook.get_connection(conn_id)

# Use connection information to connect to the external system

Q18. What is the Backfill feature in Airflow and when would you use it? (Workflow Scheduling)

The Backfill feature in Airflow is used to run DAGs for dates in the past.

How to Answer:
Explain what backfilling is and why it is valuable in a workflow scheduler. Describe the scenarios where backfilling would be appropriate and how Airflow handles it.

My Answer:

• Backfilling is the process of executing a DAG for a range of dates in the past to ensure that data processing is consistent and complete. This can be useful when implementing a new DAG or when you’ve made a change that needs to be applied retroactively.

Airflow automatically performs backfills when necessary, aligning with the DAG’s schedule interval and start date. When you create a new DAG or update an existing one with a past start date, Airflow schedules executions for all the intervals between the start date and the current date that have not yet been run.

You would use backfilling in the following scenarios:

• When a new DAG is added and you need to process historical data.
• If there was an outage or failure that prevented tasks from running at their scheduled times.
• When changes to a DAG need to be applied to past data for consistency.

Q19. How does Airflow handle task dependencies and retries? (Task Management)

Airflow manages task dependencies using a Directed Acyclic Graph (DAG) structure, and it has mechanisms in place to handle retries of tasks that fail.

How to Answer:
Discuss how to define dependencies between tasks in an Airflow DAG and how to configure task retries. Explain the benefits of having a robust system to manage task dependencies and retries.

My Answer:

• Task Dependencies: In Airflow, you define dependencies using the set_upstream, set_downstream methods, or the bitshift operators (>> and <<). This ensures that tasks are executed in a certain order.

For example, to set task B to run after task A:

task_a >> task_b

This code means that task B will not start until task A has successfully completed.

• Retries: Airflow allows you to configure retries at the task level, specifying how many times a failed task should be retried before marking it as failed. You can also define a delay between retries.

Here’s how you could configure a task to retry up to 3 times with a 5-minute delay between each retry:

from airflow import DAG
from airflow.operators.python_operator import PythonOperator
from datetime import datetime, timedelta

default_args = {
    'owner': 'airflow',
    'start_date': datetime(2023, 1, 1),
    'retries': 3,
    'retry_delay': timedelta(minutes=5),
}

dag = DAG('example_dag', default_args=default_args, schedule_interval=timedelta(days=1))

def my_task():
    # Task implementation
    pass

task = PythonOperator(
    task_id='my_task',
    python_callable=my_task,
    dag=dag,
)

Q20. Can you give an example of a custom Operator you might create? (Custom Development)

Creating custom Operators in Airflow allows for extending its capabilities to meet specific requirements not addressed by the built-in operators.

How to Answer:
Discuss what custom operators are and provide a scenario where a custom operator would be beneficial. Include a code snippet of a simple custom operator.

My Answer:
A custom operator is an extension of Airflow’s built-in BaseOperator class, which allows you to define your own task logic.

For example, let’s say we need an operator to send a message to Slack whenever a DAG starts or finishes.

from airflow.models import BaseOperator
from slack_sdk import WebClient
from slack_sdk.errors import SlackApiError

class SlackNotificationOperator(BaseOperator):

    def __init__(self, slack_token, channel, message, *args, **kwargs):
        super(SlackNotificationOperator, self).__init__(*args, **kwargs)
        self.slack_token = slack_token
        self.channel = channel
        self.message = message

    def execute(self, context):
        client = WebClient(token=self.slack_token)
        try:
            response = client.chat_postMessage(channel=self.channel, text=self.message)
            print(response)
        except SlackApiError as e:
            print(f"Error sending message to Slack: {e.response['error']}")

This custom operator can then be used within your DAGs to send notifications to Slack. You would pass the necessary Slack API token, channel, and message when initializing the operator in your DAG.

Q21. Describe how Airflow’s Plugins system works. (Extensibility)

Airflow’s plugin system allows developers to extend the core functionalities by writing their own custom plugins. Plugins can be used to add operators, hooks, macros, executors, and web views that are not available in Airflow by default. Here’s how it works:

• Operators: Extend the functionality of tasks that can be created in a DAG.
• Hooks: Create new connections to external systems and databases.
• Executors: Add new ways to execute tasks.
• Macros: Provide new Jinja templates functionalities.
• Web Views: Add new pages or modify existing pages in the Airflow web interface.

To implement a plugin, you can create a Python file within the plugins directory of your Airflow environment. This file should define a class that inherits from AirflowPlugin and includes the custom objects you want to introduce. Once the plugin is created and placed in the correct directory, Airflow will automatically detect and register it.

For example, here’s a simple plugin that adds a custom operator:

from airflow.plugins_manager import AirflowPlugin
from airflow.models import BaseOperator
from airflow.utils.decorators import apply_defaults

class CustomOperator(BaseOperator):
    @apply_defaults
    def __init__(self, my_param, **kwargs):
        super(CustomOperator, self).__init__(**kwargs)
        self.my_param = my_param

    def execute(self, context):
        # Custom execution logic here
        pass

# Defining the plugin class
class MyCustomPlugin(AirflowPlugin):
    name = "my_custom_plugin"
    operators = [CustomOperator]

Q22. What are the benefits of using Templating in Airflow? (Dynamic Workflow Generation)

Templating in Airflow is highly beneficial as it allows for dynamic parameterization of tasks using Jinja templating language. This enables the creation of flexible and reusable workflows. The benefits include:

• Dynamic Task Configuration: Easily pass parameters to tasks at runtime.
• Code Reusability: Write generic tasks that can be used in multiple DAGs with different parameters.
• Workflow Simplification: Reduce the number of hardcoded values and simplify the structure of DAGs.
• Conditional Logic: Implement conditional operations based on the templated variables within tasks.

For instance, you could use templating to dynamically set file paths, query parameters, or other task-specific variables based on the execution date or other external factors.

Q23. How can you use SubDAGs, and what are their benefits and drawbacks? (Workflow Optimization)

SubDAGs are a way to organize a complex workflow into smaller, more manageable sections. They are essentially DAGs nested within parent DAGs. SubDAGs can be beneficial in certain circumstances:

• Modularity: They break down complex workflows into logical, manageable parts.
• Reusability: They allow for the reuse of common patterns in multiple parts of a workflow or across different workflows.
• Parallelism: They can encapsulate a group of tasks to be executed in parallel, separate from the parent DAG’s scheduling.

However, SubDAGs also have some drawbacks:

• Complexity: They can add an additional layer of complexity to the workflow management.
• Execution Overhead: Since SubDAGs are DAGs themselves, they have their own scheduling and execution overhead.
• Error Propagation: Issues in a SubDAG can sometimes be harder to diagnose and can affect the parent DAG.

Here’s a simple example of how you might define a SubDAG:

from airflow import DAG
from airflow.operators.subdag_operator import SubDagOperator
from datetime import datetime

def subdag(parent_dag_name, child_dag_name, args):
    with DAG(
        dag_id=f"{parent_dag_name}.{child_dag_name}",
        default_args=args,
        schedule_interval="@daily",
    ) as dag:
        # Define tasks for the SubDAG here
        pass

    return dag

main_dag = DAG(
    'main_dag',
    default_args={
        'start_date': datetime(2021, 1, 1)
    },
    schedule_interval="@daily",
)

sub_dag_task = SubDagOperator(
    task_id='sub_dag_task',
    subdag=subdag('main_dag', 'sub_dag_task', {'start_date': datetime(2021, 1, 1)}),
    default_args={'start_date': datetime(2021, 1, 1)},
    dag=main_dag,
)

Q24. Discuss how you would integrate Airflow with cloud services like AWS or GCP. (Cloud Integration)

Airflow can be integrated with cloud services like AWS and GCP by using the respective hook and operator classes provided by Airflow’s apache-airflow-providers packages.

For AWS, you would typically use AwsHook to establish a connection and operators like S3ToRedshiftOperator or EmrAddStepsOperator for specific tasks. For GCP, you would use GoogleCloudBaseHook and operators like BigQueryOperator or DataflowTemplateOperator.

Here are the general steps to integrate Airflow with a cloud service:

1. Install Provider Packages: Ensure the appropriate provider packages are installed.
2. Set Up Connections: Configure Airflow connections with the cloud provider’s credentials.
3. Use Hooks and Operators: Use the relevant hooks and operators in your DAGs to interact with cloud services.

Q25. Explain how Airflow ensures that tasks are executed in the correct order. (Execution Order & Dependency Resolution)

Airflow ensures that tasks are executed in the correct order through its dependency resolution mechanism, which is based on the concept of Directed Acyclic Graphs (DAGs). Within a DAG, tasks can have dependencies defined, which dictate the order of execution.

Here are the key components:

• DAGs: Define a collection of tasks and their dependencies.
• Tasks: Individual operations or steps in a workflow.
• Upstream and Downstream: Defines the relationship between tasks. A task must finish before its downstream tasks can start.

The following table outlines the key parameters related to task execution order:

Parameter	Description
depends_on_past	If set to True, a task will only run if the previous run for the task succeeded.
wait_for_downstream	When True, a task will wait for downstream tasks from the previous run to finish.
retries	The number of retries that should be attempted on failure.
retry_delay	The time to wait between retries.
priority_weight	A higher weight allows a task to run earlier when resources are limited.
trigger_rule	Defines the rule by which the task decides to run (e.g., all_success, one_failed, etc.).

Tasks in a DAG can be set up with dependencies using the set_upstream/set_downstream methods, or the >>/<< operators, ensuring they are executed in the order defined by the developer:

from airflow import DAG
from airflow.operators.dummy_operator import DummyOperator
from datetime import datetime

with DAG('my_dag', start_date=datetime(2021, 1, 1)) as dag:
    task1 = DummyOperator(task_id='task1')
    task2 = DummyOperator(task_id='task2')
    task3 = DummyOperator(task_id='task3')

    task1 >> task2 >> task3

In this example, task2 will only run after task1 has completed, and task3 will run after task2 has completed.

4. Tips for Preparation

To excel in an Airflow interview, dedicate time to mastering its core concepts and architecture. Start by reviewing the official Airflow documentation to understand the latest features and best practices. Practice by setting up your own Airflow environment to gain hands-on experience with DAGs, operators, and troubleshooting common issues.

Strengthen your knowledge in Python, as it’s crucial for creating custom operators and scripts within Airflow. Also, consider familiarizing yourself with containerization tools like Docker, which often accompany Airflow in workflows. Soft skills such as problem-solving and effective communication are equally important, as Airflow roles often require collaboration and clear articulation of complex ideas.

5. During & After the Interview

In the interview, clarity and confidence are key. Articulate your understanding of Airflow with structured answers and relevant examples. Interviewers often seek candidates who demonstrate a proactive approach to learning and a genuine interest in workflow automation. Show that you can think critically about process improvements and scalability.

Avoid common pitfalls such as not asking questions or showing inflexibility in problem-solving. Prepare a set of intelligent questions about the company’s use of Airflow, their development practices, or the challenges they face. This can demonstrate engagement and a forward-thinking mindset.

After the interview, promptly send a personalized thank-you email to express your gratitude for the opportunity and to reaffirm your interest in the role. This can leave a positive and lasting impression. Typically, companies may take a few days to a few weeks to respond with feedback or next steps, so use this time to reflect on your interview performance and continue honing your skills.