apache/incubator-airflow
Incubator Airflow
This project is a Python workflow orchestration platform and programmatic data pipeline engine used to author, schedule, and monitor complex data pipelines. It functions as a directed acyclic graph manager and scheduler, allowing users to define data movement and transformation tasks as code to ensure precise execution order and maintainability.
The platform distinguishes itself by treating workflows as code, enabling pipelines to be versioned and tested through a standard programming language. It utilizes a system of extensible operators to encapsulate integration logic and employs a templating engine to inject runtime variables and parameters into pipeline definitions.
The system covers broad capability areas including data pipeline automation, dependency-aware task execution, and historical data backfilling. It also provides a web-based monitoring dashboard for real-time progress visualization and performance tracking of workflow execution history.
Features
- Data Pipeline Automation - Provides a comprehensive system for defining and executing structured data transformation and aggregation workflows.
- Data Pipeline Orchestration - Provides a platform for defining, scheduling, and monitoring complex sequences of data processing tasks and their dependencies.
- Workflow Scheduling And Orchestration - Triggering complex data processes based on specific time intervals or external events to ensure reliable execution.
- Data Pipeline Orchestrators - Acts as a data pipeline orchestrator managing execution sequences of dependent transformation tasks.
- Metadata and State Management - Manages the persistence of task status and execution history in a relational database to coordinate distributed state.
- Data Engineering Pipelines - Provides a framework for automated collection, transformation, and loading of data across diverse sources.
- Python Data Pipeline Frameworks - Provides a Python-based framework for building and managing complex batch data workflows and DAGs.
- Task Dependency Management - Implements a dependency-aware execution engine to ensure tasks run in the correct order based on a defined graph.
- Workflow Schedulers - Features a scheduler that triggers data pipeline executions based on cron expressions, time intervals, or external events.
- Task Schedulers - Includes a scheduler that periodically scans metadata to trigger persistent background tasks.
- Distributed Worker Orchestration - Provides a distributed orchestration environment to dispatch tasks across a fleet of remote worker nodes.
- Data Pipeline Definitions - Allows pipelines to be authored as Python code, making them maintainable and versionable through standard software engineering practices.
- DAG-Based Dependency Resolution - Implements a DAG engine to determine the precise execution order of interdependent pipeline tasks.
- Directed Acyclic Graph Engines - Implements a DAG engine to ensure precise execution order and failure recovery for modular nodes.
- Operator-Based Task Abstractions - Employs an extensible system of operators to encapsulate reusable integration logic for pipeline tasks.
- Programmatic Workflow Authoring - Allows authoring data pipelines as code to ensure they are maintainable, versionable, and testable.
- Pipeline Monitoring Dashboards - Ships a web-based dashboard for tracking the status and health of data pipelines.
- Workflow Management Dashboards - Offers a management interface for monitoring and interacting with pipeline execution environments.
- Custom Task Operator Extensions - Allows developers to extend pipeline capabilities by defining and registering custom task operators.
- Backfill Managers - Includes capabilities to execute workflows over past date ranges to recover missing data or correct historical records.
- Template-Based Logic Engines - Uses a template-based logic engine to inject runtime variables and dates into task definitions.
- Pipeline Definition Templating - Provides a templating system to apply dynamic parameters and customizations to pipeline definitions.
- Execution History Tracking - Tracks execution history and status of workflows to identify failures and ensure data reliability.
- Real-time Pipeline Progress Visualization - Provides a web interface to visualize real-time pipeline progress and troubleshoot execution issues.
- Scheduling - Programmatically authors and schedules complex data workflows.
- Workflow and Pipeline Automation - Workflow automation and scheduling system for data pipelines.
- Workflow Scheduling - Programmatically author, schedule, and monitor complex workflows.
- General Purpose Orchestration - Python-based platform for managing directed acyclic graphs of tasks.
- Workflow Scheduling - Platform for programmatically authoring, scheduling, and monitoring complex workflows.
- Example Applications - System for authoring and monitoring data pipelines.
