Data Engineering Community (DEC)

CI/CD for Airflow Pipelines Your Airflow DAG is code. So treat it like code. 📌Instead of changing production DAGs manually, you can have a workflow like: Code ↓ Git ↓ Pull Request ↓ Tests ↓ Review ↓ Merge ↓ Deploy

Scheduling Is More Than @daily 📌One of the easiest things to do in Airflow is schedule a DAG: >>> schedule="@daily" But production scheduling is more complicated than that.

Retries Are Not a Magic Fix Your pipeline fails. Airflow says: >>> Retrying... Great. But should every failure be retried? Consider an API timeout. That's potentially a temporary problem, so retrying could make sense. Now consider invalid SQL.

Your Pipeline Failed. Now What? Your pipeline has been running successfully for two weeks. 📌Then one morning: FAILED. What do you do? First, don't immediately rerun everything. Find the failed task and check its logs. 📌You might discover: ◉ An API timeout

Airflow vs Prefect vs Dagster Once you start learning orchestration, you'll quickly discover that Airflow isn't the only option. You'll come across tools like Prefect and Dagster too. 📌So what's the difference?

Writing Your First Airflow DAG Enough theory. Let's build something. Imagine we're pulling data from an API, transforming it with Python and loading it into PostgreSQL. 📌Our DAG needs to handle three main tasks: ◉ Extract → Transform → Load

Airflow Tasks and Operators Now that we understand DAGs, let's talk about the actual work happening inside them. A task represents a unit of work that Airflow needs to execute. 📌That work could be: ◉ Running a Python function ◉ Executing SQL ◉ Running a Bash command

What Exactly Is a DAG? You've probably heard: “Create a DAG in Airflow.” But what exactly is a DAG? DAG stands for Directed Acyclic Graph. The name sounds more complicated than the idea. A DAG is simply a way of representing tasks and the relationships between those tasks.

🎉 Happy Birthday, Chizoba Eze ! 🎂 Today, we celebrate you, not just for the amazing person you are, but also for the creativity and dedication you bring to the Data Engineering Community as our graphics designer and volunteer. Thank you for bringing our ideas to life

Why Data Engineers Need Orchestration A Python pipeline can work perfectly when you run it yourself. But what happens when it needs to run every morning? What if the API fails? What if one task depends on another? What if the database is unavailable? What if you need