Apache Airflow

Apache Airflow is an open-source platform used to programmatically author, schedule, and monitor complex workflows and data pipelines. As a cornerstone tool in the Big Data ecosystem, it allows developers to define their workflows as code, specifically as Directed Acyclic Graphs (DAGs) in Python, making them dynamic, versionable, and maintainable. Airflow is used to orchestrate intricate sequences of tasks, such as ETL/ELT jobs, by managing dependencies, scheduling execution times, and providing a rich user interface for visualizing pipeline status, logs, and overall performance, thereby ensuring that data processing jobs run reliably and in the correct order.

1. Introduction to Apache Airflow
   1. Workflow Orchestration Fundamentals
      1. Definition of Workflow Orchestration
      2. Benefits of Orchestration in Data Engineering
      3. Comparison with Manual Scheduling
      4. Orchestration vs Automation
   2. Airflow's Role in the Modern Data Stack
      1. Integration with Data Warehouses
      2. Integration with Data Lakes
      3. Orchestration of Data Pipelines
      4. Comparison with Other Orchestration Tools
         1. Apache Oozie
         2. Luigi
         3. Prefect
         4. Dagster
         5. Kubeflow
   3. Key Characteristics of Airflow
      1. Dynamic Pipeline Generation
      2. Extensibility through Plugins and Providers
      3. Code-Based Configuration
      4. Scalability for Large Workloads
      5. Open Source Community and Ecosystem
      6. Rich User Interface
      7. Robust Monitoring and Alerting
   4. Use Cases for Airflow
      1. ETL and ELT Pipelines
         1. Data Extraction
         2. Data Transformation
         3. Data Loading
         4. Data Quality Validation
      2. Machine Learning Workflows
         1. Model Training Pipelines
         2. Model Evaluation and Validation
         3. Model Deployment
         4. Feature Engineering
      3. Report Generation and Analytics
         1. Automated Report Scheduling
         2. Data Aggregation for Reporting
         3. Dashboard Data Preparation
      4. Infrastructure Automation
         1. Resource Provisioning
         2. Automated Backups
         3. System Maintenance Tasks
      5. Business Process Automation
         1. File Processing Workflows
         2. API Integration Tasks
         3. Notification Systems