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Airflow orchestration

Advanced⏱ 16 min read📅 Updated: 2026-02-17

Introduction

Imagine nee oru restaurant manager 👨‍🍳. Every morning:

6 AM: Vegetables order pannanum
7 AM: Kitchen prep start
8 AM: Staff assignment
9 AM: Restaurant open

Idha nee manually daily pannureenga — one day nee sick leave. Yaaru handle pannu? 😱

Apache Airflow exactly this problem solve pannum — but for data pipelines! Airbnb 2014 la create pannanga, now world's most popular workflow orchestrator 🌍

Real example — E-commerce company:

Every night 12 AM: Extract sales data from database
12:30 AM: Clean and transform data
1 AM: Load into data warehouse
1:30 AM: Generate reports
2 AM: Email reports to stakeholders

Idha manual ah daily run pannureenga ah? One step fail aana? Next steps ah stop pannanum? Airflow handles everything automatically! 🚀

What is Orchestration?

Orchestration = Multiple tasks ah coordinate panni, correct order la, correct time la run panradhu 🎼

Music orchestra madhiri think pannunga:

🎻 Violin first play pannum
🥁 Drums join aagum
🎺 Trumpet specific moment la enter aagum
🎵 Conductor (Airflow) controls everything

Without orchestration:

Tasks random order la run aagum ❌
One task fail aana downstream tasks ah stop panna mudiyaadhu ❌
Retry logic manual ah handle pannanum ❌
Monitoring and alerting illai ❌

With Airflow orchestration:

Tasks defined order la run aagum ✅
Failure handling automatic ✅
Retry with backoff built-in ✅
Beautiful UI for monitoring ✅
Email/Slack alerts on failure ✅

DAG — The Core Concept

DAG = Directed Acyclic Graph — Airflow oda heart ❤️

Term	Meaning	Example
Directed	Tasks have direction (A → B)	Extract → Transform
Acyclic	No circular loops	A → B → A ❌ not allowed
Graph	Network of connected tasks	Visual pipeline

Simple DAG example:

code

Extract_Data → Clean_Data → Transform_Data → Load_to_Warehouse → Generate_Report

Complex DAG — parallel tasks:

code

                    ┌→ Clean_Sales ──┐
Extract_Sales ──→   │                │→ Join_Data → Load_DW
Extract_Users ──→   └→ Clean_Users ──┘

DAG define panna Python use pannuvaanga:

python

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

with DAG(
    'ecommerce_etl',
    start_date=datetime(2026, 1, 1),
    schedule_interval='@daily',
    catchup=False,
) as dag:

    extract = PythonOperator(
        task_id='extract_data',
        python_callable=extract_function,
    )

    transform = PythonOperator(
        task_id='transform_data',
        python_callable=transform_function,
    )

    load = PythonOperator(
        task_id='load_data',
        python_callable=load_function,
    )

    extract >> transform >> load  # Dependencies!

>> operator — "this runs after that" nu define pannum. Clean and Pythonic! 🐍

Airflow Operators

💡 Tip

🔧 Operators = Pre-built task types. Common ones:

- PythonOperator — Run any Python function

- BashOperator — Run shell commands

- PostgresOperator — Execute SQL queries

- S3ToRedshiftOperator — AWS data transfer

- EmailOperator — Send notification emails

- SlackWebhookOperator — Slack alerts

- DockerOperator — Run Docker containers

- KubernetesPodOperator — K8s pod launch

Custom operator venum na? BaseOperator extend panni own operator ezhudhalaam! Community 1000+ providers maintain pannuvaanga 🌐

Airflow Architecture

🏗️ Architecture Diagram

Airflow internal ah eppadi work aagum:

```
┌─────────────────────────────────────────────────┐
│                  AIRFLOW SYSTEM                  │
│                                                  │
│  ┌──────────┐    ┌──────────┐    ┌──────────┐  │
│  │   Web    │    │ Scheduler│    │  Metadata │  │
│  │  Server  │◄──►│          │◄──►│    DB     │  │
│  │  (UI)    │    │ (Brain)  │    │(PostgreSQL│  │
│  └──────────┘    └────┬─────┘    └──────────┘  │
│                       │                          │
│              ┌────────┼────────┐                 │
│              ▼        ▼        ▼                 │
│         ┌────────┐┌────────┐┌────────┐          │
│         │Worker 1││Worker 2││Worker 3│          │
│         │(Celery)││(Celery)││(Celery)│          │
│         └────────┘└────────┘└────────┘          │
│              │        │        │                 │
│              ▼        ▼        ▼                 │
│     ┌─────────────────────────────────┐         │
│     │    Task Execution Layer          │         │
│     │  Python | Bash | SQL | API      │         │
│     └─────────────────────────────────┘         │
└─────────────────────────────────────────────────┘
```

**Components:**
- **Web Server** — UI dashboard, DAG visualization, logs view
- **Scheduler** — DAGs parse panni tasks queue ku send pannum (brain 🧠)
- **Executor** — Tasks actually run pannum (Local, Celery, Kubernetes)
- **Metadata DB** — DAG runs, task states, variables store pannum
- **Workers** — Actual computation nadakkum (distributed setup la)

Scheduling & Triggers

Airflow powerful scheduling options provide pannum:

Cron-based schedules:

python

schedule_interval='0 6 * * *'    # Daily 6 AM
schedule_interval='0 */2 * * *'  # Every 2 hours
schedule_interval='0 9 * * 1'    # Every Monday 9 AM

Preset schedules:

Preset	Meaning	Cron
@daily	Once a day midnight	0 0 * * *
@hourly	Every hour	0 * * * *
@weekly	Every Sunday	0 0 * * 0
@monthly	First of month	0 0 1 * *
@once	Run only once	—

Timetable (Airflow 2.x):

python

from airflow.timetables.trigger import CronTriggerTimetable

dag = DAG(
    'business_hours_dag',
    timetable=CronTriggerTimetable(
        '0 9-17 * * 1-5',  # Business hours only!
        timezone='Asia/Kolkata',
    ),
)

Data-aware scheduling (Airflow 2.4+):

python

# DAG B runs only after DAG A produces dataset
my_dataset = Dataset('s3://bucket/sales_data/')

# Producer DAG
@dag(schedule='@daily')
def producer():
    @task(outlets=[my_dataset])
    def produce():
        # Write data...
        pass

# Consumer DAG — auto-triggered!
@dag(schedule=[my_dataset])
def consumer():
    @task
    def consume():
        # Read data...
        pass

XComs — Task Communication

Tasks between data share panna XComs (Cross-Communications) use pannuvaanga:

python

# Task 1: Push data
def extract(**context):
    data = {'rows': 1000, 'source': 'postgres'}
    context['ti'].xcom_push(key='extract_meta', value=data)

# Task 2: Pull data
def transform(**context):
    meta = context['ti'].xcom_pull(
        task_ids='extract_data',
        key='extract_meta'
    )
    print(f"Processing {meta['rows']} rows from {meta['source']}")

TaskFlow API (Airflow 2.x) — much cleaner:

python

@dag(schedule='@daily', start_date=datetime(2026, 1, 1))
def ecommerce_pipeline():

    @task
    def extract():
        return {'rows': 1000, 'source': 'postgres'}

    @task
    def transform(data: dict):
        print(f"Processing {data['rows']} rows")
        return {'processed': data['rows']}

    @task
    def load(result: dict):
        print(f"Loading {result['processed']} rows to warehouse")

    data = extract()
    result = transform(data)  # Auto XCom!
    load(result)

ecommerce_pipeline()

⚠️ Warning: XComs metadata DB la store aagum — large data pass panna vendaam! File paths or S3 keys pass pannunga, actual data illa.

Airflow Best Practices

⚠️ Warning

⚠️ Production Airflow Tips:

1. Idempotent tasks — Same task re-run pannaalum same result varanum

2. No heavy computation in DAG file — DAG parse every 30s nadakkum, slow code irukkak koodaadhu

3. Use Connections & Variables — Hardcode credentials panna vendaam!

4. Set retries and retry_delay — Transient failures ku automatic retry

5. Use pools — Resource limit set pannunga (max 10 DB connections)

6. Set SLAs — Task romba time eduthaa alert varum

7. Atomic tasks — One task = one unit of work

8. Test DAGs locally — airflow dags test my_dag 2026-01-01

Error Handling & Monitoring

Production la error handling critical:

Retry configuration:

python

default_args = {
    'retries': 3,
    'retry_delay': timedelta(minutes=5),
    'retry_exponential_backoff': True,
    'max_retry_delay': timedelta(minutes=30),
    'email_on_failure': True,
    'email': ['data-team@company.com'],
}

Callbacks:

python

def on_failure(context):
    dag_id = context['dag'].dag_id
    task_id = context['task_instance'].task_id
    send_slack_alert(f"❌ {dag_id}.{task_id} FAILED!")

def on_success(context):
    send_metric('task_success', 1)

task = PythonOperator(
    task_id='critical_task',
    python_callable=my_function,
    on_failure_callback=on_failure,
    on_success_callback=on_success,
)

SLA monitoring:

python

task = PythonOperator(
    task_id='must_finish_fast',
    python_callable=my_function,
    sla=timedelta(hours=2),  # Alert if takes > 2 hours
)

Monitoring stack:

📊 Airflow UI — DAG runs, task logs, Gantt charts
📈 Prometheus + Grafana — Metrics dashboard
🔔 PagerDuty/OpsGenie — On-call alerting
📝 ELK Stack — Centralized logging

Executor Types

Airflow different executors support pannum:

Executor	Use Case	Scalability
SequentialExecutor	Development only	❌ Single task
LocalExecutor	Small teams	⚡ Multi-process
CeleryExecutor	Production standard	🚀 Distributed workers
KubernetesExecutor	Cloud-native	🌐 Auto-scaling pods
CeleryKubernetesExecutor	Hybrid	🔥 Best of both

KubernetesExecutor advantages:

Each task runs in isolated pod 🐳
Different resources per task (heavy task = more CPU/RAM)
Auto-scaling — idle pods terminate
No persistent workers — cost efficient 💰

python

# Task-specific K8s config
from kubernetes.client import models as k8s

resource_config = k8s.V1ResourceRequirements(
    requests={'memory': '2Gi', 'cpu': '1'},
    limits={'memory': '4Gi', 'cpu': '2'},
)

task = PythonOperator(
    task_id='heavy_task',
    python_callable=process_large_data,
    executor_config={
        'KubernetesExecutor': {
            'resources': resource_config,
            'image': 'my-custom-image:latest',
        }
    },
)

Real-World DAG Example

Complete production DAG — E-commerce daily ETL:

python

from airflow.decorators import dag, task
from airflow.providers.postgres.operators.postgres import PostgresOperator
from airflow.providers.amazon.aws.transfers.s3_to_redshift import S3ToRedshiftOperator
from datetime import datetime, timedelta

@dag(
    schedule='@daily',
    start_date=datetime(2026, 1, 1),
    catchup=False,
    tags=['ecommerce', 'etl'],
    default_args={
        'retries': 2,
        'retry_delay': timedelta(minutes=5),
        'email_on_failure': True,
    },
)
def ecommerce_daily_etl():

    @task
    def extract_orders(ds=None):
        """Extract yesterday's orders from production DB"""
        query = f"SELECT * FROM orders WHERE date = '{ds}'"
        df = run_query(query)
        path = f"s3://data-lake/raw/orders/{ds}.parquet"
        df.to_parquet(path)
        return {'path': path, 'count': len(df)}

    @task
    def extract_users(ds=None):
        """Extract updated user profiles"""
        query = f"SELECT * FROM users WHERE updated_at = '{ds}'"
        df = run_query(query)
        path = f"s3://data-lake/raw/users/{ds}.parquet"
        df.to_parquet(path)
        return {'path': path, 'count': len(df)}

    @task
    def transform(orders_meta, users_meta):
        """Join and transform data"""
        orders = pd.read_parquet(orders_meta['path'])
        users = pd.read_parquet(users_meta['path'])
        result = orders.merge(users, on='user_id')
        path = "s3://data-lake/processed/daily_summary.parquet"
        result.to_parquet(path)
        return {'path': path, 'count': len(result)}

    @task
    def data_quality_check(meta):
        """Validate transformed data"""
        df = pd.read_parquet(meta['path'])
        assert len(df) > 0, "No data found!"
        assert df['revenue'].min() >= 0, "Negative revenue!"
        return True

    @task
    def notify(meta):
        """Send completion notification"""
        send_slack(f"✅ ETL complete! {meta['count']} rows processed")

    # DAG flow
    orders = extract_orders()
    users = extract_users()
    transformed = transform(orders, users)
    quality = data_quality_check(transformed)
    quality >> notify(transformed)

ecommerce_daily_etl()

Think About This

📋 Copy-Paste Prompt

🤔 **Scenario:** Unga company la 50 DAGs irukku, daily run aagum. One DAG fail aana:
- Downstream 10 DAGs blocked
- Data warehouse stale data show pannum
- Business team wrong decisions eduppanga

**Questions:**
1. Eppadi idha prevent pannuvenga?
2. Failure detection speed eppadi improve pannuvenga?
3. Which executor choose pannuvenga — Celery or Kubernetes? Why?

Hint: Monitoring, alerting, circuit breakers, and data quality checks think pannunga! 🛡️

Common DAG Patterns

Production la frequently use aagura patterns:

1. Fan-out / Fan-in:

code

           ┌→ Process_Region_1 ──┐
Start ──→  ├→ Process_Region_2 ──┤→ Aggregate
           └→ Process_Region_3 ──┘

2. Branching:

python

@task.branch
def check_data_size(ti):
    count = ti.xcom_pull(task_ids='extract')
    if count > 1000000:
        return 'process_large'
    return 'process_small'

3. Dynamic task mapping (Airflow 2.3+):

python

@task
def get_files():
    return ['file1.csv', 'file2.csv', 'file3.csv']

@task
def process(file):
    print(f"Processing {file}")

# Dynamic! Tasks auto-created based on output
process.expand(file=get_files())

4. Sensor — wait for condition:

python

from airflow.sensors.s3_key_sensor import S3KeySensor

wait_for_file = S3KeySensor(
    task_id='wait_for_data',
    bucket_name='data-lake',
    bucket_key='raw/daily/{{ ds }}.parquet',
    timeout=3600,  # Wait max 1 hour
    poke_interval=300,  # Check every 5 minutes
)

Airflow vs Alternatives

Market la other options um irukku:

Feature	Airflow	Prefect	Dagster	Luigi
Maturity	🏆 Most mature	Growing	Growing	Legacy
UI	Good	Better	Best	Basic
Python-native	✅	✅	✅	✅
Cloud managed	AWS, GCP	Prefect Cloud	Dagster+	❌
Community	Largest	Medium	Medium	Small
Learning curve	Medium	Easy	Medium	Easy
Dynamic pipelines	Airflow 2.3+	Built-in	Built-in	Limited
Data lineage	Datasets (2.4+)	Basic	Excellent	❌

When to choose Airflow:

Enterprise environment with existing investment 🏢
Need vast ecosystem of providers/operators
Team already knows Airflow
Battle-tested for mission-critical pipelines

🏁 🎮 Mini Challenge

Challenge: Complex Airflow DAG Multi-Step

Production-like DAG design practice:

Setup - 5 min:

bash

pip install apache-airflow
airflow db init

DAG Design - 20 min:

python

from airflow.decorators import dag, task
from datetime import datetime

@dag(schedule='@daily', start_date=datetime(2026, 1, 1))
def analytics_pipeline():

    @task
    def extract_data():
        print("Extracting from 3 sources...")
        return {'records': 100000}

    @task
    def validate_data(data: dict):
        print(f"Validating {data['records']} records...")
        assert data['records'] > 0
        return data

    @task
    def transform_data(data: dict):
        print(f"Transforming {data['records']} records...")
        # Complex transformation here
        return {'processed': data['records'] * 0.95}

    @task
    def load_data(data: dict):
        print(f"Loading {data['processed']} records...")
        # Load to warehouse

    @task
    def generate_report(data: dict):
        print(f"Generating report for {data['processed']} records...")

    # DAG Flow
    extracted = extract_data()
    validated = validate_data(extracted)
    transformed = transform_data(validated)

    # Parallel tasks
    load_task = load_data(transformed)
    report_task = generate_report(transformed)

    # Final alert (depends on both)
    [load_task, report_task]

analytics_pipeline()

Run & Monitor - 10 min:

bash

airflow scheduler &
airflow webserver
# Visit http://localhost:8080 – DAG visualization perfect ah visible!

Learning: DAG design = task modeling + dependency management. Clean architecture foundation! 🏗️

💼 Interview Questions

Q1: Airflow DAG design – best practices?

A: Atomic tasks (one task = one unit). Idempotent (re-run safe). No heavy computation in DAG file (parsed every 30s). Dependencies clear. Retries and timeouts. Error handling callbacks. Monitoring. Documentation. Testing (airflow dags test). Production readiness!

Q2: TaskFlow API vs traditional operators?

A: TaskFlow (modern Airflow 2+) cleaner, Pythonic. XCom automatic. Traditional more explicit, boilerplate. TaskFlow preferred now. But team experience matters – learning curve if new. Adopt gradually!

Q3: SLA miss – oru task 2 hours exceed?

A: Alert trigger! Investigation: Network issue? Data volume spike? Resource bottleneck? Code inefficiency? Historical trends check. Tune: Add retry, increase timeout, parallelize, optimize code. Monitoring essential to catch trends early!

Q4: DAG run history – old runs delete pannum safe ah?

A: Carefully! Historical data useful – debugging, understanding patterns. Keep last 90 days minimum. Archive old (database → backup). Metadata DB optimize (cleanup old task logs). But NEVER delete current runs – SLA, audit trail needed!

Q5: Distributed execution (Celery vs Kubernetes)?

A: Celery: Simpler, persistent workers, cost-effective for predictable load. Kubernetes: Complex, auto-scaling, pods isolated, expensive but flexible. Choice: Team skills, load pattern, cost. Start Celery (smaller teams), scale Kubernetes (large enterprises). Either works!

Frequently Asked Questions

❓ Airflow na enna simple ah?

Airflow is a workflow orchestration tool. Simple ah sonna — cron jobs on steroids! Multiple tasks ah define panni, dependencies set panni, schedule panni, monitor panna oru platform.

❓ Airflow vs Luigi vs Prefect — enna difference?

Airflow is the industry standard — huge community, mature ecosystem. Luigi is simpler but less features. Prefect is modern alternative with better developer experience. Most companies Airflow choose pannuvaanga for enterprise support.

❓ Airflow free ah?

Apache Airflow open-source and free! Managed services like MWAA (AWS), Cloud Composer (GCP), Astronomer cost money. Self-host pannaalum server cost irukku.

❓ Airflow learn panna enna prerequisites venum?

Python must — DAGs Python la ezhudhuvaanga. SQL basics, Linux commands, Docker knowledge helpful. Data engineering concepts therinjaa faster ah learn pannalaam.

❓ Airflow real-time processing ku use pannalaamaa?

No! Airflow is for batch orchestration — scheduled workflows. Real-time ku Kafka, Flink use pannunga. Airflow oda minimum schedule interval 1 minute, but typically hourly/daily runs ku use pannuvaanga.

Summary

Airflow Orchestration — Key Takeaways: 🎯

DAG = Directed Acyclic Graph — pipeline as code 📝
Operators = Pre-built task types (Python, Bash, SQL, etc.)
Scheduler = Brain — triggers tasks at right time ⏰
XComs = Task-to-task data sharing (keep it small!)
Executors = How tasks run (Local → Celery → Kubernetes)
TaskFlow API = Modern, cleaner way to write DAGs 🐍
Sensors = Wait for external conditions
Dynamic task mapping = Runtime task generation
Error handling = Retries, callbacks, SLAs — production must-haves! 🛡️
Data-aware scheduling = Dataset-triggered DAGs (Airflow 2.4+)

Airflow learn pannaa data engineering career la massive advantage! Almost every data team uses it. Start with simple DAGs, gradually complex patterns learn pannunga 🚀

🧠Knowledge Check

Quiz 1 of 1

Airflow la `extract >> [clean_sales, clean_users] >> join_data` nu define pannaa enna nadakkum?

0 of 1 answered

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Exam Prep

Airflow orchestration

Introduction

What is Orchestration?

DAG — The Core Concept

Airflow Operators

Airflow Architecture

Scheduling & Triggers

XComs — Task Communication

Airflow Best Practices

Error Handling & Monitoring

Executor Types

Real-World DAG Example

Think About This

Common DAG Patterns

Airflow vs Alternatives

🏁 🎮 Mini Challenge

💼 Interview Questions

Frequently Asked Questions

Summary