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Empower Your Data Workflow Orchestration and Automation


Are you ready to embark on a journey into the world of data workflow orchestration and automation with Apache Airflow? "Mastering Apache Airflow" is your comprehensive guide to harnessing the full potential of this powerful platform for managing complex data pipelines. Whether you're a data engineer striving to optimize workflows or a business analyst aiming to streamline data processing, this book equips you with the knowledge and tools to master the art of Airflow-based workflow automation.

Mastering Apache Airflow

  • 1.Introduction to Apache Airflow
    1.1.What is Apache Airflow?
    1.2.Workflow Automation and Its Importance
    1.3.Key Features and Benefits
    1.4.Real-world Use Cases
    2.Getting Started with Airflow Installation and Configuration
    2.1.Installation and Setup of Airflow
    2.2.Configuration Options and Best Practices
    2.3.Exploring the Airflow Web Interface
    2.4.Understanding the Directory Structure
    3.Defining Workflows with Directed Acyclic Graphs (DAGs)
    3.1.Introduction to DAGs and Tasks
    3.2.Defining DAGs using Python Scripts
    3.3.Task Definition and Dependency Management
    3.4.Scheduling and Triggering Workflows
    4.Operators and Executors
    4.1.Overview of Different Types of Operators
    4.2.Utilizing Built-in Operators for Various Tasks
    4.3.Creating Custom Operators
    4.4.Selecting the Right Executor for Your Deployment
    5.Managing Dependencies and Task States
    5.1.Handling Task Dependencies with Trigger Rules
    5.2.Policies for Retrying and Error Handling
    5.3.Understanding and Managing Task States
    5.4.Monitoring and Logging Tasks
    6.Data Sensors and Data Quality Checks
    6.1.Waiting for External Data with Sensors
    6.2.Ensuring Data Availability and Reliability
    6.3.Implementing Data Quality Checks
    6.4.Best Practices for Ensuring Data Quality
    7.Advanced Workflow Patterns and Strategies
    7.1.Dynamic DAG Generation and Templating
    7.2.Conditional Execution and Branching
    7.3.Dynamic Triggering of Downstream Tasks
    8.Integrating with External Systems and Services
    8.1.Working with Databases, APIs, and Cloud Services
    8.2.Leveraging Hooks and Connections for Integration
    8.3.Automating ETL Processes with External Systems
    8.4.Real-time Data Streaming with External Triggers
    9.Scaling and High Availability
    9.1.Strategies for Horizontal and Vertical Scaling
    9.2.Distributing Airflow across Clusters
    9.3.Achieving High Availability in Production
    9.4.Monitoring and Managing Large-scale Deployments
    10.Data Pipeline Orchestration and Management
    10.1.Building End-to-End Data Pipelines
    10.2.Coordinating Tasks Across Technologies
    10.3.Integrating with Data Processing Frameworks
    10.4.Managing Complex Data Workflows
    11.Extending Airflow with Plugins and Customizations
    11.1.Exploring Airflow's Extensibility Features
    11.2.Developing Custom Operators and Sensors
    11.3.Creating and Sharing Plugins
    11.4.Enhancing Airflow through Customization
    12.Best Practices for Successful Airflow Deployments
    12.1.Design Principles for Scalable Workflows
    12.2.Managing Metadata and Database Migrations
    12.3.Ensuring Security and Access Control
    12.4.Continuous Integration and Deployment Strategies
    13.Real-World Use Cases and Case Studies
    13.1.Implementing ETL Processes with Airflow
    13.2.Automating Machine Learning Pipelines
    13.3.Data Warehousing and Analytics Workflows
    13.4.Real-time Event Processing and Monitoring
    14.Future Trends and Beyond
    14.1.Evolving Landscape of Workflow Orchestration
    14.2.Integration with Emerging Technologies
    14.3.Community Contributions and Advancements
    14.4.Predictions for the Future of Apache Airflow
    15.1.Airflow CLI Reference
    15.2.Airflow Web Interface Reference
    15.3.Sample DAG Scripts for Reference
    15.4.Glossary of Terms
    15.5.Additional Resources and References
    About the author

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