In-depth tutorials, production DAG patterns, best practices, and community resources for data engineers at every level.
Handpicked deep-dives and guides that every data engineer should read.
A hands-on beginner's guide to creating your first DAG in Apache Airflow. We cover the core concepts — tasks, operators, scheduling, and dependencies — with complete, runnable code.
Prashant Singh
Senior Data Engineer
Learn how to configure robust retry strategies in Airflow using retries, retry_delay, exponential backoff, and on_failure_callback. Includes real-world patterns for handling flaky APIs, database timeouts, and more.
Neha Agarwal
Data Platform Engineer
A complete production guide to deploying Apache Airflow on Kubernetes. Covers the official Helm chart, KubernetesExecutor vs CeleryExecutor, resource quotas, pod templates, and GitSync DAG deployment.
Prashant Singh
Senior Data Engineer
Everything from Airflow basics to advanced Kubernetes deployments and monitoring.
The most recent tutorials, guides, and deep-dives from our authors.
Explore the latest updates, features, and improvements in Apache Airflow 3.1.8, including crucial scheduler fixes, Task SDK advantages, and security patches.
A hands-on beginner's guide to creating your first DAG in Apache Airflow. We cover the core concepts — tasks, operators, scheduling, and dependencies — with complete, runnable code.
Learn how to configure robust retry strategies in Airflow using retries, retry_delay, exponential backoff, and on_failure_callback. Includes real-world patterns for handling flaky APIs, database timeouts, and more.
A complete production guide to deploying Apache Airflow on Kubernetes. Covers the official Helm chart, KubernetesExecutor vs CeleryExecutor, resource quotas, pod templates, and GitSync DAG deployment.
Learn how to integrate Apache Airflow with dbt (data build tool) using the dbt Cloud provider and Cosmos. Covers scheduling dbt runs, handling failures, passing artifacts between runs, and best practices for the dbt + Airflow stack.
Master Airflow's Dynamic Task Mapping feature to create a variable number of task instances at runtime. Covers .expand(), .partial(), expand_kwargs(), and real-world patterns for parallel data processing.
No fluff, no clickbait. Just high-quality, technically-accurate content for engineers who build real data infrastructure.
All DAG examples are tested, version-pinned, and ready to adapt for production use. Complete with error handling and logging.
We go deep on every topic. From Airflow internals to XCom architecture to scheduler tuning — nothing is hand-waved.
Every tutorial covers security implications. Secrets backends, RBAC, network policies — we treat security as a core feature.
Learn to identify and fix performance bottlenecks, tune the scheduler, right-size your workers, and cut cloud costs.
Content is authored by real engineers with battle scars. Stories from real-world Airflow deployments at scale.
We track every Airflow release and immediately update our tutorials. You'll never find outdated provider examples here.
Jump directly to the most-used references and tools.
Definitions for every Airflow concept, from DAGs to XCom to Pools.
Airflow vs Prefect vs Dagster vs Mage — when to choose what.
Unit tests, integration tests, and CI strategies for your DAGs.
A structured path from zero to production Airflow engineer.
Follow our structured learning path or jump directly into topics relevant to you. Every article builds on real-world scenarios.
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