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Developer tools · #apacheairflow
Learn how the TaskFlow API lets you write tasks as plain Python functions, automatically handling dependencies and data passing, and understand its pickle‑based limitations.
Identify why Airflow tasks stay queued due to scheduler deadlocks, check logs, DB transactions, and process counts, then apply targeted fixes and know when to escalate.
Stop copy-pasting DAG files. Learn how to use dynamic DAG generation in Apache Airflow to scale your pipelines using configuration files while avoiding Scheduler performance pitfalls.
A technical guide for choosing between Local, Celery, and Kubernetes executors in Apache Airflow based on scaling needs, infrastructure constraints, and fault tolerance.
python
kubernetes