**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 ```