Introduction to Apache Airflow & Workflow Orchestration
OPTIMIZING DATA PIPELINES WITH APACHE AIRFLOW
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.
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.
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.
Apache Airflow Architecture
Components Overview:
• Scheduler
• Worker Nodes
• Metadata Database
• Executors
• Web Server
• Diagram showcasing data flow within Airflow.
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.
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.
Apache Airflow vs Other Orchestration Tools
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.
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