Building and Scaling an Airflow Data Processing Cluster: A Comprehensive Guide
Introduction Apache Airflow has become the de‑facto standard for orchestrating complex data pipelines. Its declarative, Python‑based DAG (Directed Acyclic Graph) model makes it easy to express dependencies, schedule jobs, and handle retries. However, as data volumes grow and workloads become more heterogeneous—ranging from Spark jobs and Flink streams to simple Python scripts—running Airflow on a single machine quickly turns into a bottleneck. Enter the Airflow data processing cluster: a collection of machines (or containers) that collectively execute the tasks defined in your DAGs. A well‑designed cluster not only scales horizontally, but also isolates workloads, improves fault tolerance, and integrates tightly with the broader data ecosystem (cloud storage, data warehouses, ML platforms, etc.). ...