Better Rasters, Smarter Workflows, and a Reality Check on GIS Jobs
- Author: matt@forrest.nyc
- Full Title: Better Rasters, Smarter Workflows, and a Reality Check on GIS Jobs
- Type:
Highlights
- Here are the non-spatial reasons why people use Airflow—and why it’s showing up everywhere from fintech to climate analytics:
1. Scheduling without chaos
Airflow replaces fragile cron jobs with readable, version-controlled DAGs. You can define complex schedules, dependencies, and retries in Python, not by guessing at timestamps.
2. Dependency management that actually makes sense
Your task doesn’t run until the one before it succeeds. It’s simple, but powerful. No more worrying about your script running on incomplete data or half-processed files.
3. Observability built in
Airflow gives you a visual DAG, logs for every task, and retry buttons when something goes sideways. You know exactly what ran, when, and why it failed, without SSH-ing into a random VM or EC2 box.
4. Modularity and reusability
Each task is just a function. Want to swap out the source from Postgres to S3? Easy. Want to run the same logic across 50 datasets? Done.
5. It plays nicely with everything
Airflow isn’t opinionated about what you’re running. Bash, Python, Spark, SQL, cloud functions, if you can script it, you can run it in Airflow. (View Highlight)