Week 12 - Orchestration
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Introduction to Orchestration
Airflow Fundamentals
Scheduling and Triggers
Sequential Pipeline Steps
Parameterized Runs and Backfills
Testing DAGs
Monitoring and Debugging
Deploying to Shared Airflow
Practice
Gotchas & Pitfalls
Assignment: Orchestration
Glossary: Week 12
Career relevance: Week 12
Going Further
History of Data Orchestration
Slides (PDF)
Week 12 - Orchestration
Welcome to Week 12! You have built pipelines that ingest, transform, and model data. Now it's time to make them run automatically and reliably. This week covers orchestration: scheduling your pipelines, chaining steps together, handling failures, and monitoring production runs.
By the end of this week, you will have a fully orchestrated pipeline that runs on a schedule, chains ingestion and transformation steps in the correct order, supports parameterized runs and backfills, and surfaces errors through Airflow's logs and the UI.
How this week helps you get hired
Junior data engineers in the Netherlands are usually trusted with existing pipelines before they build new ones. Teams expect you to:
- understand why a pipeline failed and what to do about it
- backfill historical dates safely without creating duplicates
- communicate incidents clearly while a run is broken
- avoid silent data corruption when reruns happen
Week 12 trains exactly these skills. This is strong portfolio evidence because it shows operational ownership: not just writing code that works once, but running it reliably every morning.
Learning goals
- Understand what orchestration means and why it is essential for production data pipelines
- Set up and navigate Apache Airflow including DAGs, tasks, operators, and the web UI
- Define cron-based schedules and event-driven triggers to automate pipeline runs
- Chain pipeline steps sequentially so ingestion runs before dbt transformation
- Use parameterized runs and backfills to reprocess data for specific dates or date ranges
- Monitor pipeline health and debug failed runs using Airflow logs
- Develop DAGs locally with the Astro CLI and deploy them to the class's shared Airflow via a shared Git repo
- Work alongside classmates on a single shared scheduler without interfering with each other's pipelines
By the end of this week, you should be able to explain
- how to trigger and debug a failed DAG run in Airflow
- why ds (logical date) matters for reruns
- how your DAG prevents duplicate processing during backfills
Chapters
- Introduction to Orchestration
- Airflow Fundamentals
- Scheduling and Triggers
- Sequential Pipeline Steps
- Parameterized Runs and Backfills
- Testing DAGs
- Monitoring and Debugging
- Deploying to Shared Airflow
- Practice
- Gotchas & Pitfalls
- Assignment: Orchestration
Supplementary
Glossary: Week 12
Career relevance: Week 12
Going Further
History of Data Orchestration
Slides (PDF)