Introduction to Apache Airflow & Workflow Orchestration

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_datedatetime(2024,
  1, 1))
• task1 DummyOperator(task_id'start', dagdag)
• task2 DummyOperator(task_id'end', dagdag)
• task1 gtgt task2
• Running the DAG and monitoring in the Web UI.

10
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• Course Highlights
• Hands-on training with real-world projects.
• Expert trainers from the industry.
• Certification guidance for Apache Airflow.
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