How does Apache Airflow integrate with ETL processes?

Apache Airflow integrates with ETL processes by providing a programmable framework to define, schedule, and monitor workflows. At its core, Airflow allows developers to model ETL pipelines as Directed Acyclic Graphs (DAGs), where each node represents a task (e.g., extracting data), and edges define dependencies between tasks. This structure ensures tasks execute in the correct order, and Airflow’s scheduler handles retries, backfilling, and error handling. For example, a daily ETL job to load sales data might include tasks to extract from an API, clean the data, and load it into a database, all orchestrated through a single DAG.

Airflow simplifies ETL implementation through reusable components like operators and hooks. Operators define individual tasks—such as the PythonOperator to run transformation logic or the BashOperator to execute shell scripts. Hooks abstract connections to external systems (e.g., databases, cloud storage), reducing boilerplate code. For instance, a DAG might use the PostgresHook to connect to a PostgreSQL database for extraction, a PythonOperator to apply data quality checks, and the S3Hook to upload processed files to AWS S3. Sensors, a type of operator, can pause a workflow until a condition is met (e.g., waiting for a file to land in S3 before transformation). This modularity allows developers to mix built-in tools with custom code for flexibility.

Monitoring and scalability are key strengths. Airflow’s web UI provides real-time visibility into task statuses, logs, and execution history, making it easier to troubleshoot failed tasks or analyze performance. For example, if a data extraction task fails due to a transient API error, developers can review logs, adjust parameters, and retry the task without restarting the entire pipeline. Airflow also supports scaling through executors like CeleryExecutor for distributed task execution, which is critical for large ETL jobs. By combining these features, Airflow ensures ETL processes are repeatable, maintainable, and adaptable to changing data requirements.