Apache Airflow
Fewer than 5 people recommend it so far, so there's no written verdict. Here is what commenters said.
Why people recommend it
“Products like Apache airflow tend to have value not because of the persistence layer, but because they force workflows into DAGs”
“they literally could have just stuck a nice frontend on top of it and written a backend data transfer library. job done.”
Caveats and dissent
“it feels much better than previous orchestrators I used like Prefect or Airflow”
“19.2% of cryptographic algorithms used are not quantum-safe”
Show 1 more
“I am currently ditching both airflow and dbt”
Often compared with
Who recommends it in their own words
From the makers not counted in the ranking
“Astronomer is a primary contributor to Apache Airflow”
Threads where it came up
- Ask HN: Who wants to be hired? (July 2026) 2 mentions
- Apache Burr: Build reliable AI agents and applications 2 mentions
- SQLite is all you need for durable workflows 2 mentions
- Command-line Tools can be 235x Faster than your Hadoop Cluster (2014) 2 mentions
- Ask HN: Who is hiring? (September 2026) 1 mention
- Ask HN: Who is hiring? (August 2026) 1 mention
Also written as Airflow. Quotes are verbatim; the verdict and theme labels are written by a model from these comments, and every clause links to its sources.
All comments 25
“Airflow”
“Products like Apache airflow tend to have value not because of the persistence layer, but because they force workflows into DAGs”
“dbt, and Airflow”
“Temporal, Airflow, NATS”
“they literally could have just stuck a nice frontend on top of it and written a backend data transfer library. job done.”
“why would I use this instead of airflow with some custom built agent bits?”
“use a workflow engine (Airflow, Temporal, etc)”
Microsoft has their own Durable Task framewor[1] for that kind of stuff, and it supports both running as a self-hosted standalone service like temporal, and running serverless on Azure Functions. It actually predated airflow, temporal, etc., IIRC. This one seems to be more database-specific use case. The advantage is probably that you can track the exact state of the job in the database itself, rather than having to
“none of the other orchastrators I used (like AirFlow) made me write workflows.IsNewCode/IsOldCode”
“it feels much better than previous orchestrators I used like Prefect or Airflow”
“using Python, SQL, Snowflake/dbt/Airflow, and Anaplan”
“Check out Airflow and Dagster.”
“19.2% of cryptographic algorithms used are not quantum-safe”
“when running Airflow on spot instances”
“used by me and many machine learning folks”
“Built ETL pipelines using Airflow + PostgreSQL”
“Data/Workflows: Temporal, Airflow, Spark”
“drop it straight into Airflow”
“Airflow”
“airflow and dbt have literally nothing to do with the size of the data and can be useful, or overkill, at any size”
“I am currently ditching both airflow and dbt”
“Temporal, Airflow, Spark”
“Today, we mostly use Python, Postgres, Airflow and Pyspark.”
“Postgres, Airflow and Pyspark”
“heavyweight orchestrators (Airflow).”