Apache Airflow is the leading open-source platform for workflow orchestration in data engineering. It allows you to programmatically author, schedule, and monitor complex data pipelines as Directed Acyclic Graphs (DAGs). Airflow is highly extensible and integrates with almost every modern data warehouse, processing engine, and cloud provider, making it an essential tool for orchestrating enterprise data workflows. Explore our collection of 9 in-depth articles about Airflow on DataEngineer Hub. Each tutorial provides practical, hands-on guidance with real-world examples to help you master Airflow concepts and best practices.
For the past two years, the standard pattern for running LLM calls in Airflow was a PythonOperator that imported the OpenAI client, called the API, and returned the result as…
The 2 a.m. page said the pipeline “succeeded.” The dashboard was green. And the finance team was still staring at yesterday’s numbers, because one task in a forty-task DAG had…
I once inherited an Airflow repo with 214 DAG files that were, functionally, the same DAG. Each one extracted a table from a source system, loaded it into Snowflake, and…
For years, the pattern was: Airflow sits in one corner of your infrastructure, dbt runs on a server somewhere else, they pass data between each other via manual credential handoffs…
I evaluated Prefect seriously. Ran it in a staging environment for six weeks. Built three real flows. Had the internal conversation about migrating. And then stayed with Airflow. That was…
TL;DR→ Delta Lake is easier to start with, especially if you’re already on Databricks→ Iceberg wins on engine flexibility — works natively with Spark, Flink, Trino, Snowflake, and more without…
How I Wired Snowflake’s Native dbt Projects to Airflow — And Finally Got True End-to-End Orchestration I’ll be honest with you — for a long time I was running dbt…
The Moment Everything Changed It was a Tuesday morning when I finally snapped. My dbt project had grown to 147 models, and the daily run was taking 2 hours and…
In the world of data, consistency is king. Manually running scripts to fetch and process data is not just tedious; it’s prone to errors, delays, and gaps in your analytics….