Apache Airflow

Key features of Apache Airflow:

1. DAGs (Directed Acyclic Graphs): Workflows in Airflow are modeled as DAGs, where each task is a node and the arrows indicate the order of execution.
2. Scalability: Airflow can be scaled to manage thousands of tasks per day and is suitable for large, complex workflows.
3. Flexibility and Extensibility: You can define workflows with Python, which makes Airflow extremely flexible and easy to integrate with other tools and systems.
4. Monitoring and Logging: Airflow provides a web interface for monitoring workflows and accessing detailed logs of each task.
5. Integrations: It supports a wide range of integrations with databases, APIs, cloud services, and other tools, including Hadoop, Spark, and AWS.
6. SLA Management: You can set Service Level Agreements (SLAs) for tasks so that you get alerts when certain time limits are exceeded.

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