ETL Made Easy: Simplify Complex Data Flows with Airflow#

Introduction#

Extract, Transform, and Load (ETL) workflows are critical to making sense of vast amounts of data. In nearly every data-driven organization, the need to efficiently move, cleanse, and restructure data cannot be overstated. Unfortunately, many organizations still rely on outdated manual scripts or ad-hoc processes that are brittle, difficult to debug, and prone to error.

Enter Apache Airflow. Airflow is an open-source platform to programmatically author, schedule, and monitor workflows. Created at Airbnb and adopted by thousands of organizations worldwide, Airflow has become the de facto standard for orchestrating complex data pipelines. With it, you can transform tangled data flows into structured workflows that are transparent, reproducible, and easy to maintain.

In this blog post, we will start at the very basics of ETL. We will gradually build our way up to advanced concepts in Airflow: from understanding DAGs (Directed Acyclic Graphs) and operators, to scheduling, advanced flow controls, and more. By the end, you will have hands-on examples, best practices, and a clear path toward implementing Airflow for production-grade ETL at your organization.

What is ETL?#

ETL (Extract, Transform, Load) is a systematic process for collecting data from various sources, modifying it to fit operational needs, and loading it into a final destination (often a data warehouse or data lake). Let’s break the acronym down:

Extract: Gather data from one or multiple sources. Sources can be diverse—databases, APIs, files, streaming platforms, or even manually entered data.
Transform: Clean, format, and reshape the data to align with analytical or operational constraints. Common transformations include data type conversions, joining multiple data sets, applying business rules, and more.
Load: Push the transformed data into a target system, typically a database or a data warehouse.

ETL pipelines form the foundation for data analytics, data science, machine learning, and business intelligence. High-quality and well-organized data is a necessity for extracting actionable insights.

Why Airflow for ETL?#

Plenty of tools attempt to solve ETL challenges. Traditional solutions may involve complex proprietary infrastructures or require massive changes in how teams operate. Airflow offers a more flexible, code-oriented approach with tangible benefits:

Pythonic and Extensible: Airflow is written in Python, and you write your DAGs in Python. This means it is relatively easy to integrate with the Python ecosystem and adapt Airflow to your unique requirements.
Modular Architecture: Airflow divides tasks into discrete blocks (Operators), making your ETL pipeline modular and maintainable.
Scalable: You can run Airflow on a single machine for simple workflows or scale it to a distributed cluster for high-volume data processing.
Open-Source Community: Airflow has a vibrant ecosystem with a broad selection of ready-to-use operators and integrations. You can find best practices and get help from a large community of users and contributors.
Observability: Airflow’s user interface provides a clear view of your DAG runs. You can see logs, track failures, retry tasks, and adjust schedules with minimal friction.

In short, Airflow is a powerful tool that helps you orchestrate and maintain data flows in a reproducible, testable, and observable manner.

Getting Started with Airflow#

1. Installation#

Installing Airflow typically involves setting up a Python environment and installing the core Airflow package. You may also need additional providers to integrate with services like Amazon S3, Google Cloud Storage, or other data sources.

Below is a simple example of how you might install Airflow in a clean virtual environment:

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# Create and activate a virtual environment
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python3 -m venv airflow_env
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source airflow_env/bin/activate
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# Install Airflow (version 2.5.0 is just an example version)
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pip install apache-airflow==2.5.0 --constraint "https://raw.githubusercontent.com/apache/airflow/constraints-2.5.0/constraints-3.8.txt"

The constraints file ensures that all dependencies match the Airflow release, minimizing version conflicts.

2. Initialize the Airflow Database#

Once Airflow is installed, you must initialize its metadata database:

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# Initialize the Airflow metadata database
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airflow db init

Airflow uses this database to store information about DAG runs, task states, and more. By default, it uses SQLite for quick setup, but for production environments, a more robust database like PostgreSQL or MySQL is recommended.

3. Create an Admin User#

To log into the Airflow UI, create a user with at least “Admin” privileges:

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airflow users create \
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  --username admin \
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  --password admin \
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  --firstname Air \
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  --lastname Flow \
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  --role Admin \
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  --email admin@example.com

You can customize these details as needed.

4. Start the Airflow Webserver and Scheduler#

Finally, start the Airflow web server and scheduler in separate terminals or as background processes:

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# Start the webserver (Default port is 8080)
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airflow webserver --port 8080
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# In another terminal session, start the scheduler
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airflow scheduler

Now, navigate to http://localhost:8080 in your browser. Log in with the username and password you created, and you’ll have access to the Airflow UI.

Creating Your First Airflow DAG#

In Airflow, a DAG (Directed Acyclic Graph) represents the flow of tasks. It defines the relationships and order in which the tasks need to run. Each node in the DAG is a task, and edges define dependencies.

Below is a simple “hello world” DAG:

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from datetime import datetime
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from airflow import DAG
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from airflow.operators.bash import BashOperator
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default_args = {
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    'start_date': datetime(2023, 1, 1),
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    'retries': 1
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}
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with DAG(
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    dag_id='hello_world_dag',
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    default_args=default_args,
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    schedule_interval='@daily',
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    catchup=False
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) as dag:
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    task_hello = BashOperator(
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        task_id='say_hello',
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        bash_command='echo "Hello Airflow!"'
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    )
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    task_hello

Breaking it down:#

default_args: A dictionary of default parameters passed to each task (start date, retries, etc.).
with DAG(…) as dag: Creates a DAG context so that tasks defined inside are automatically associated with it.
BashOperator: Executes a bash command. Here it just echoes “Hello Airflow!”.

Placing this file in the Airflow “dags” directory (by default, ~/airflow/dags) allows Airflow to detect and schedule it. You’ll see the DAG appear in the Airflow UI DAG list.

Understanding Airflow Operators#

Operators are the building blocks of tasks in Airflow. An operator typically defines a single, atomic action. Airflow comes with many operators, including:

BashOperator: Run a bash script or command.
PythonOperator: Execute Python callables.
EmailOperator: Send emails.
SimpleHttpOperator: Make HTTP requests.
Sensor: Pause execution until a condition is met (e.g., a file arrives in a specific location).

Operators can be chained to form a DAG. The relationship between tasks can be defined using the bitwise shift operators:

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task_a >> task_b  # task_a must finish before task_b starts
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task_b << task_c  # task_c must finish before task_b starts

Example Operator Setup#

Below is an example of defining tasks with different operators:

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from airflow.operators.python import PythonOperator
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import requests
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def fetch_data(url):
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    response = requests.get(url)
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    return response.json()
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with DAG(dag_id='multi_op_dag', ... ) as dag:
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    t1 = BashOperator(
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        task_id='print_date',
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        bash_command='date'
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    )
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    t2 = PythonOperator(
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        task_id='fetch_api_data',
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        python_callable=fetch_data,
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        op_kwargs={'url': 'https://api.example.com/data'}
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    )
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    t3 = BashOperator(
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        task_id='process_data',
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        bash_command='echo "Process data here..."'
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    )
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    t1 >> t2 >> t3

Here, t1 prints the current date, then t2 fetches data from an external API, and finally t3 processes the data (in this example, it just echoes a message).

Data Transformations Made Easy#

The “Transform” step in ETL often requires multiple tasks or Python scripts to massage data. With Airflow, you can leverage:

PythonOperator to run inline transformations in Python.
Custom Operators for more complex or reusable logic.
Task Groups to logically group related tasks.

Inline Python Transformations#

Here’s a simple workflow illustrating a typical data transformation:

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from airflow import DAG
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from airflow.operators.python import PythonOperator
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from datetime import datetime
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def extract_data():
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    return [
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        {"id": 1, "value": 10},
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        {"id": 2, "value": 20}
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    ]
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def transform_data(ti):
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    data = ti.xcom_pull(task_ids='extract')
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    # Add a 'processed_value' key
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    for record in data:
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        record['processed_value'] = record['value'] * 2
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    ti.xcom_push(key='transformed_data', value=data)
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def load_data(ti):
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    transformed_data = ti.xcom_pull(task_ids='transform', key='transformed_data')
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    # Simulate loading to a database
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    print("Loading data:", transformed_data)
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default_args = {
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    'start_date': datetime(2023, 1, 1)
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}
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with DAG('simple_etl', default_args=default_args, schedule_interval='@daily', catchup=False) as dag:
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    t_extract = PythonOperator(
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        task_id='extract',
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        python_callable=extract_data
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    )
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    t_transform = PythonOperator(
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        task_id='transform',
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        python_callable=transform_data
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    )
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    t_load = PythonOperator(
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        task_id='load',
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        python_callable=load_data
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    )
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    t_extract >> t_transform >> t_load

In this DAG:

extract_data retrieves data (mimicked by returning a list of dictionaries).
transform_data doubles each “value” and appends that result into a new key called “processed_value.”
load_data prints the final result. In a real scenario, you might insert into a database or push the data to a data warehouse.

Notice how data is passed between tasks using XCom (Cross-Communication). We pull and push data via ti.xcom_pull and ti.xcom_push.

Scheduling and Monitoring Your Workflows#

A key feature of Airflow is scheduling: automatically triggering DAG runs at specified intervals. Airflow supports cron expressions and presets like @daily, @hourly, and so on.

schedule_interval: If set to None, the DAG will only run when triggered manually.
For interval-based scheduling, you can use a cron expression or an Airflow preset string.

Scheduling Example#

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with DAG(
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    'daily_etl',
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    default_args=default_args,
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    schedule_interval='0 3 * * *',  # Runs at 3 AM every day
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    catchup=False
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) as dag:
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    ...

Monitoring#

Airflow provides a web UI to monitor DAGs:

Graph View: Visualize the DAG structure.
Tree View: Inspect historical runs.
Task Instances: View and search logs for each task run.
Code View: Inspect the Python code that defines the DAG.

You can set up SLAs (Service Level Agreements) to track if a task takes longer than expected. Airflow can also send email alerts or trigger notifications when tasks fail or succeed.

Advanced Topics in Airflow#

1. Task Groups#

If you have multiple tasks performing related transformations, grouping them can enhance readability. For example:

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from airflow.utils.task_group import TaskGroup
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with DAG('grouped_etl', ...) as dag:
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    extract = PythonOperator(...)
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    with TaskGroup("transformations") as transformations:
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        transform_task1 = PythonOperator(...)
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        transform_task2 = PythonOperator(...)
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        transform_task1 >> transform_task2
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    load = PythonOperator(...)
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    extract >> transformations >> load

2. Sensors#

Sensors in Airflow allow tasks to “wait” until a certain condition is met. Examples:

FileSensor: Wait until a file lands in S3.
ExternalTaskSensor: Wait for another DAG’s task to complete.
TimeSensor: Wait until a certain time of day.

Sensors help create data dependencies in your workflows. For instance, your pipeline might wait for a partner to place a CSV file in an FTP folder before starting the transformation step.

3. Branching and Conditional Logic#

Branching allows you to define conditional paths in your DAG. For instance, you might choose a different set of transformations based on the outcome of a previous task:

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from airflow.operators.branch import BranchPythonOperator
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def branch_logic(**context):
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    value = context['ti'].xcom_pull(task_ids='some_task')
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    if value > 10:
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        return 'task_a'
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    else:
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        return 'task_b'
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branch_task = BranchPythonOperator(
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    task_id='branch_logic',
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    python_callable=branch_logic,
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    provide_context=True
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)
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task_a = PythonOperator(...)
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task_b = PythonOperator(...)
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branch_task >> [task_a, task_b]

4. Dynamic Task Mapping#

Dynamic Task Mapping (introduced in Airflow 2.3) allows you to create tasks at runtime based on the outputs of upstream tasks. This is very powerful for processing data sets that vary in size or splitting tasks across multiple files.

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from airflow.decorators import task, dag
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@task
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def get_file_list():
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    return ['file1.csv', 'file2.csv']
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@task
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def process_file(file_name: str):
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    print(f"Processing {file_name}")
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@dag(schedule_interval='@daily', start_date=datetime(2023,1,1), catchup=False)
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def dynamic_mapping_dag():
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    files = get_file_list()
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    process_file.expand(file_name=files)
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dag_instance = dynamic_mapping_dag()

5. Airflow Plugins and Custom Operators#

You can extend Airflow’s functionality by creating custom plugins:

Custom Operators to handle tasks specific to your organization (e.g., custom data ingestion).
Hooks to interface with external services (e.g., custom database or API connections).
Macros to define custom logic for your DAG configs.

Example: Building a Robust ETL Pipeline#

Below is a more detailed example to demonstrate how you might create a complete ETL pipeline with multiple steps, a sensor, dynamic branching, and error handling.

Scenario#

You receive daily data files from an SFTP server. You must extract the data, apply business rules based on file type, transform the data, and then load it into a PostgreSQL database. If no files arrive, you want the pipeline to fail early.

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from airflow import DAG
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from airflow.providers.sftp.sensors.sftp import SFTPSensor
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from airflow.operators.python import PythonOperator, BranchPythonOperator
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from airflow.operators.bash import BashOperator
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from datetime import datetime, timedelta
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def determine_file_type(file_name):
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    if file_name.endswith('.csv'):
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        return 'csv_processing'
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    elif file_name.endswith('.json'):
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        return 'json_processing'
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    else:
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        return 'invalid_file'
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def process_csv(file_name):
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    # CSV processing logic here
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    print(f"Processing CSV file: {file_name}")
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def process_json(file_name):
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    # JSON processing logic here
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    print(f"Processing JSON file: {file_name}")
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def load_to_postgres():
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    # Loading data to Postgres
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    print("Data loaded into Postgres.")
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default_args = {
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    'start_date': datetime(2023,1,1),
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    'retries': 2,
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    'retry_delay': timedelta(minutes=5),
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}
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with DAG(
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    dag_id='robust_etl_pipeline',
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    default_args=default_args,
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    schedule_interval='@daily',
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    catchup=False
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) as dag:
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    # Sensor to check if file is present on SFTP
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    sensor_task = SFTPSensor(
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        task_id='check_sftp',
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        sftp_conn_id='my_sftp_connection',
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        path='/data/inbound/file_*',
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        timeout=30,
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        poke_interval=10
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    )
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    # Branching to determine file type
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    branch_task = BranchPythonOperator(
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        task_id='branch_task',
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        python_callable=determine_file_type,
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        op_kwargs={'file_name': '/data/inbound/file_20230101.csv'}
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    )
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    # CSV path
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    csv_processing = PythonOperator(
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        task_id='csv_processing',
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        python_callable=process_csv,
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        op_kwargs={'file_name': '/data/inbound/file_20230101.csv'}
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    )
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    # JSON path
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    json_processing = PythonOperator(
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        task_id='json_processing',
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        python_callable=process_json,
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        op_kwargs={'file_name': '/data/inbound/file_20230101.json'}
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    )
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    # Invalid file path
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    invalid_file = BashOperator(
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        task_id='invalid_file',
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        bash_command='echo "Invalid file type. Terminating pipeline."'
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    )
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    # Load step
77
    load_data = PythonOperator(
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        task_id='load_to_postgres',
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        python_callable=load_to_postgres
80
    )
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    sensor_task >> branch_task
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    branch_task >> [csv_processing, json_processing, invalid_file]
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    csv_processing >> load_data
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    json_processing >> load_data

In this pipeline:

SFTPSensor waits for the presence of a file matching a certain pattern. If no file arrives by the timeout, the task fails, halting the pipeline.
BranchPythonOperator decides which processing path (CSV or JSON) to take based on the file’s extension. If it’s neither .csv nor .json, the pipeline logs an invalid file message and ends.
PythonOperator tasks read and transform the data accordingly.
BashOperator is triggered if the file is invalid.
PythonOperator loads data into PostgreSQL as the final step.

Best Practices and Tips#

1. Use Version Control#

Keep your DAG files in Git or another version control system. This allows you to track changes, review new contributions, and revert if necessary.

2. Modularize Code#

Separate logic into multiple Python files or modules. For instance:

Put transformations in a transformations.py file.
Keep custom operators in an operators folder.
Maintain hooks in a hooks folder.

This improves readability and testability.

3. Parameterize Your Pipelines#

Instead of hardcoding values (like file paths or database credentials), use Airflow Variables or environment variables. This approach allows you to adjust pipelines without rewriting code.

4. Avoid Overloading the Scheduler with Heavy Processing#

The scheduler should only oversee job orchestration. CPU-heavy or memory-intensive tasks should happen within operators, typically on worker nodes in your distributed setup.

5. Monitoring and Alerting#

Set up email alerts, Slack notifications, or other communication methods to inform you when tasks fail or exceed SLAs. Airflow can integrate with various alerting services to keep you informed.

6. Performance Tuning#

For high-volume data, consider:

Deploying Airflow on Kubernetes or using Celery executors to distribute workloads.
Using specialized operators for big data frameworks (Spark, Hadoop, etc.).
Caching transformations to avoid reprocessing.

7. Security#

Enable authentication for the Airflow UI.
Use role-based access control (RBAC).
Store sensitive connections and credentials in Airflow Connections with proper encryption or integrate with a secrets manager like HashiCorp Vault.

Conclusion#

Building and maintaining robust ETL workflows is a high-stakes endeavor for any data-savvy organization. Apache Airflow provides a powerful, flexible, and community-supported framework to take on these challenges. Whether you are just getting started with simple Python transformations or orchestrating an enterprise-grade data platform that spans thousands of tasks and billions of records, Airflow’s core concepts remain the same:

Author workflows as code in Python.
Schedule tasks with a variety of robust options and triggers.
Monitor with a web UI that offers complete visibility.

The journey starts with installing Airflow, creating your first DAG, and building confidence in your local environment. As you grow more comfortable, you can explore advanced operators, sensors, branching, dynamic task mapping, plugins, and scaling strategies. Eventually, armed with Airflow’s best practices, you’ll create sophisticated ETL pipelines that are both reliable and easy to maintain.

Airflow is more than just an orchestrator—it is a framework that helps you transform cross-functional data engineering tasks into a single cohesive system. By leveraging the power of DAGs, you can simplify complex data flows, reduce errors, and accelerate time-to-insight. With Airflow, ETL becomes not just manageable but genuinely enjoyable to build and optimize.

Happy engineering! If you have any questions, feel free to explore the official Airflow documentation, connect with the open-source community, or share your experiences and challenges with fellow data engineers. The opportunities for growth and innovation are endless as you continue to refine and evolve your data workflows with Airflow.