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Introduction to Apache Airflow & Workflow Orchestration

OPTIMIZING DATA PIPELINES WITH APACHE AIRFLOW

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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.

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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.

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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.

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Apache Airflow Architecture

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

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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.

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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.

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Apache Airflow vs Other Orchestration Tools

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Feature

Apache Airflow

Prefect

Luigi

AWS Step Functions

Open Source

✅

✅

✅

❌

UI Monitoring

✅

✅

❌

✅

Cloud Integration

✅

✅

❌

✅

Extensibility

✅

✅

❌

❌

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Hands-on with Apache Airflow

  • Install Airflow: pip install apache-airflow

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  • Define a simple DAG:

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from airflow import DAG

from airflow.operators.dummy import DummyOperator

from datetime import datetime

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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

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  • Running the DAG and monitoring in the Web UI.

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  • Course Highlights:
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