This PPT covers the fundamentals of Apache Airflow and workflow orchestration, including its architecture, key components, and real-world use cases. It highlights how Airflow automates and manages data pipelines efficiently. The presentation also introduces the Accentfuture Apache Airflow course for hands-on learning and career growth. 🚀

1. Introduction to Apache Airflow & Workflow
Orchestration
OPTIMIZING DATA PIPELINES WITH APACHE AIRFLOW

2. What is Apache Airflow?
• Open-source workflow automation and orchestration tool.
• Developed by Apache Software Foundation.
• Manages complex workflows as Directed Acyclic Graphs
(DAGs).
• Ensures task scheduling, monitoring, and dependency
management.

3. Why Use Apache Airflow?
Scalability: Manages workflows from small tasks to large
enterprise pipelines.
Flexibility: Define workflows as Python scripts.
Extensibility: Supports plugins and integrates with cloud
services (AWS, GCP, Azure).
Monitoring: Web UI for tracking workflows and logs.
Automation: Schedule and trigger workflows efficiently.

4. Key Components of Apache Airflow
• DAGs (Directed Acyclic Graphs): Define workflows and
dependencies.
• Operators: Pre-built tasks (Bash, Python, SQL, etc.).
• Scheduler: Automates execution timing.
• Executor: Runs tasks (LocalExecutor, CeleryExecutor,
KubernetesExecutor).
• Web UI: Provides visibility into DAG runs and logs.

5. Apache Airflow Architecture
• Components Overview:
• Scheduler
• Worker Nodes
• Metadata Database
• Executors
• Web Server
• Diagram showcasing data flow within Airflow.

6. Workflow Orchestration with Apache
Airflow
• Workflow orchestration ensures smooth execution of
interconnected tasks.
• Apache Airflow enables:
• Task Dependency Management
• Dynamic Task Execution
• Error Handling & Retries
• Integration with ETL, Machine Learning, and Cloud Data
Processing.

7. Use Cases of Apache Airflow
• ETL Pipelines: Automate data extraction, transformation,
and loading.
• Data Pipeline Orchestration: Manage end-to-end data
workflows.
• Machine Learning Pipelines: Automate ML model training
and deployment.
• Cloud Integration: Workflows across AWS, GCP, and Azure.
• Real-time Data Processing: Stream processing using
Apache Kafka and Spark.

8. Apache Airflow vs Other Orchestration
Tools
Feature
Apache
Airflow
Prefect Luigi AWS Step Functions
Open Source ✅ ✅ ✅ ❌
UI Monitoring ✅ ✅ ❌ ✅
Cloud
Integration
✅ ✅ ❌ ✅
Extensibility ✅ ✅ ❌ ❌

9. Hands-on with Apache Airflow
•Install Airflow: pip install apache-airflow
•Define a simple DAG:
from airflow import DAG
from airflow.operators.dummy import DummyOperator
from datetime import datetime
dag = DAG('simple_dag', start_date=datetime(2024, 1, 1))
task1 = DummyOperator(task_id='start', dag=dag)
task2 = DummyOperator(task_id='end', dag=dag)
task1 >> task2
•Running the DAG and monitoring in the Web UI.

10. Learn Apache Airflow with
Accentfuture
• Course Highlights:
• Hands-on training with real-world projects.
• Expert trainers from the industry.
• Certification guidance for Apache Airflow.
• Career support and job placement assistance.
• Enroll Now! Visit Accentfuture for more details.