**What is Apache Airflow?**
Apache Airflow is a powerful open-source platform for programmatically authoring, scheduling, and monitoring workflows. It's particularly popular in the realm of data engineering and data science for orchestrating complex data pipelines. See [Airflow.apache.org](https://airflow.apache.org/)
**Key Concepts & Features**
- **Workflows as Code:** One of Airflow's core ideas is to represent workflows as Python code. This makes your workflows dynamic, flexible, and easy to version control.
- **DAGs (Directed Acyclic Graphs):** The building blocks of Airflow. Each [[DAG]] is a collection of tasks with defined dependencies and execution order. This visual representation enhances clarity and maintainability.
- **Operators:** Pre-built modular components that perform specific actions. Airflow has a wide array of operators for:
- Interacting with databases (e.g., MySQL, PostgreSQL)
- Loading data to cloud platforms (e.g., AWS S3, Google Cloud Storage)
- Running Spark jobs
- Executing Python scripts or Bash commands
- Triggering notifications or messages
- **Scheduler:** Airflow's scheduler handles the execution of your DAGs based on time intervals (e.g., daily, hourly) or external triggers.
- **UI:** Airflow provides a rich web-based user interface to monitor DAG runs, inspect logs, visualize task dependencies, and manage your workflows.
- **Extensibility:** You can write custom operators and plugins to integrate Airflow with other systems or tailor it to your specific needs.
**Why is Airflow Popular for Data Engineering?**
- **Scalability:** Handles large, complex data processing pipelines efficiently.
- **Reliability:** Airflow includes features like retries, failure notifications, and backfilling (rerunning past tasks) to ensure your pipelines are robust.
- **Strong Community:** Active community and extensive documentation make it easy to find support and resources.
- **Data-centric:** Airflow's design aligns well with the structure of data engineering tasks and offers many convenient features for managing data flow.
**Getting Started**
1. **Installation:** Airflow is typically installed using pip: `pip install apache-airflow`
2. **Experimentation:** Experiment with the UI and sample DAGs included with Airflow.
3. **Learning Resources:** Plenty of tutorials, the official documentation, and online courses can guide you through building your workflows.
**Use Cases**
- **ETL/ELT Pipelines:** Building robust data ingestion and transformation processes.
- **Machine Learning Pipelines:** Orchestrating the entire machine learning lifecycle (data prep, training, evaluation, deployment).
- **Data Quality and Validation Workflows:** Defining and scheduling regular checks to ensure data integrity.
- **Infrastructure Management:** Automating infrastructure tasks in cloud environments.
# References
```dataview
Table title as Title, authors as Authors
where contains(subject, "Airflow") or contains(subject, "Workflow") or contains(subject, "DAG") or contains(subject, "Directed Acyclic Graph")
sort title, authors, modified, desc
```