07.02 · Walkthrough
Airflow DAG Authoring
Create an Airflow DAG that runs an extract, transform, validate, and publish workflow on a schedule.
No video curated for this lesson yet
This lesson is written, ordered and part of the path - the video slot is the only thing still open. We are working through Everything Data lesson by lesson; 55 of 85 have their video so far.
The written notes below cover this idea in full - you lose nothing by reading instead of watching.
An Airflow DAG is the operational contract for a scheduled data pipeline: it defines task order, dependencies, retries, failure handling, and publish gates while keeping heavy business logic elsewhere. A robust extract, transform, validate, and publish flow is repeatable, observable, safe to retry, and explicit about data quality before release.
What this lesson answers
- how to structure an Airflow data pipeline DAG
- where should validation happen in an Airflow DAG
- how to make Airflow tasks safe to retry
Notes
Airflow DAG authoring is about describing a workflow as code: what tasks need to run, how they depend on each other, when the workflow should be scheduled, and how failures should be handled. A DAG, or directed acyclic graph, should represent the logical movement of data through a pipeline without containing too much business logic directly in the DAG file. For this lesson, the workflow follows a common data engineering pattern: extract data from a source system, transform it into the desired shape, validate that the output meets expectations, and publish it for downstream users or systems.
Common questions
- What should go in an Airflow DAG file?
- The DAG file should describe orchestration: the schedule, task definitions, dependencies, defaults, retries, timeouts, and failure behaviour. Keep substantial transformation logic, SQL, API calls, and environment-specific configuration in separate modules, scripts, services, or configuration. This keeps scheduler parsing cheap and makes the workflow easier to read and maintain.
- Why is validation a separate task in a data pipeline DAG?
- Validation should act as the gate between producing data and publishing it. Airflow can order work, retry failures, and record logs, but it does not prove the data is correct. A validation task can check freshness, required fields, schema expectations, row counts, or business rules before downstream consumers see the output.
- How do I make an Airflow pipeline safe to rerun?
- Design each task with clear inputs and outputs, and avoid relying on hidden mutable state. Rerunning extraction or transformation should not create duplicate records, overwrite unrelated data, or publish partial results. Use deterministic paths, idempotent writes, staging areas, and publish only after validation succeeds, so retries and backfills remain operationally safe.
Short definition: what is Airflow DAG Authoring?