What is Apache Airflow?

At its core, Airflow allows you to:

• Define workflows as DAGs (Directed Acyclic Graphs)

• Schedule tasks to run at specific intervals

• Monitor task execution in a web UI

• Integrate with multiple systems like AWS, GCP, Azure, Hadoop, Spark, Kubernetes, etc.

👉 In short: Airflow = Cron jobs on steroids + Workflow visualization + Dependency management.

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🔹 Key Features of Apache Airflow

✅ DAG-based Orchestration – Workflows are defined as Python code, giving flexibility and version control.
✅ Extensible – Easily integrates with cloud services, databases, ML frameworks, and APIs.
✅ Scalable – Can run on a single machine or scale across distributed systems with Celery/Kubernetes.
✅ Rich UI – Provides a user-friendly dashboard to monitor, retry, and debug jobs.
✅ Community Support – A strong ecosystem with thousands of plugins and operators.

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🔹 Airflow Architecture

Airflow follows a modular architecture with the following key components:

1. Scheduler – Orchestrates the execution of tasks by following DAG dependencies.

2. Executor – Defines how tasks are executed (Local, Celery, Kubernetes, etc.).

3. Workers – Execute tasks assigned by the scheduler.

4. Web Server – A rich UI for monitoring DAGs, logs, and execution status.

5. Metadata Database – Stores configurations, task states, and logs.

📌 Example workflow:

• Scheduler reads the DAG.

• Executor assigns tasks to Workers.

• Web UI shows progress and logs.

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🔹 DAGs in Airflow

A DAG (Directed Acyclic Graph) is the backbone of Airflow.

• Each DAG is a collection of tasks.

• Dependencies define the execution order.

• Tasks can run in parallel or sequentially.

Example: A Simple ETL DAG

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

def extract():
    print("Extracting data...")

def transform():
    print("Transforming data...")

def load():
    print("Loading data...")

default_args = {
    'owner': 'airflow',
    'start_date': datetime(2025, 1, 1),
    'retries': 1,
}

with DAG('etl_pipeline',
         default_args=default_args,
         schedule_interval='@daily',
         catchup=False) as dag:

    extract_task = PythonOperator(
        task_id='extract',
        python_callable=extract
    )

    transform_task = PythonOperator(
        task_id='transform',
        python_callable=transform
    )

    load_task = PythonOperator(
        task_id='load',
        python_callable=load
    )

    extract_task >> transform_task >> load_task

📌 Here’s what happens:

• The DAG runs daily (@daily).

• extract → transform → load executes sequentially.

• Airflow Web UI will display it as a pipeline flowchart.

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🔹 Installing Apache Airflow

You can install Airflow locally with pip:

pip install "apache-airflow==2.10.2" --constraint "https://raw.githubusercontent.com/apache/airflow/constraints-2.10.2/constraints-3.8.txt"

Initialize Airflow database:

airflow db init

Start Airflow services:

airflow webserver --port 8080
airflow scheduler

👉 Now visit: http://localhost:8080

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🔹 Real-World Use Cases of Airflow

🔹 Data Engineering – Automating ETL pipelines (Extract → Transform → Load).
🔹 MLOps – Orchestrating ML pipelines (Data preprocessing → Model training → Deployment).
🔹 DevOps – Scheduling system jobs, log cleanup, or CI/CD tasks.
🔹 Cloud Workflows – Managing multi-cloud data movement and processing.
🔹 Analytics – Running daily/weekly reporting jobs.

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🔹 Best Practices with Airflow

✅ Keep DAGs idempotent – running them multiple times shouldn’t cause data corruption.
✅ Use XComs for passing data between tasks.
✅ Leverage TaskGroups for better DAG organization.
✅ Set proper retry policies and alerting mechanisms.
✅ Use Airflow Variables/Connections to avoid hardcoding secrets.

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🔹 Airflow vs Other Orchestrators

📌 Conclusion: Airflow is best for general workflow orchestration, but for ML-heavy workflows, you may also explore Prefect or Kubeflow.

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🔹 Final Thoughts

Apache Airflow has become the de facto standard for workflow orchestration in the data world. Whether you are a Data Engineer, DevOps Engineer, or MLOps Engineer, learning Airflow will give you an edge in building scalable, automated, and reliable pipelines.

If you’re just starting out, begin with:

1. Installing Airflow locally.

2. Writing a simple DAG (like the ETL example).

3. Gradually integrating it with databases, APIs, and cloud services.

👉 Once mastered, Airflow can handle complex enterprise-grade workflows with ease.