# Apache Airflow 3.2.0: Data-Aware Workflows at Scale

DevFeed: [Apache Airflow 3.2.0: Data-Aware Workflows at Scale](<https://devfeed.tech/articles/apache-airflow-3-2-0-data-aware-workflows-at-scale-32541.md>)

Original publisher: [Read original article](<https://airflow.apache.org/blog/airflow-3.2.0/>)

Author: Apache Airflow

Published: 2026-04-07T00:00:00Z

Content type: release

Language: en

Sources: [Apache Airflow Blog](<https://devfeed.tech/sources/apache-airflow-blog.md>)

Topics: [airflow](<https://devfeed.tech/topics/airflow.md>), [releases](<https://devfeed.tech/topics/releases.md>), [data](<https://devfeed.tech/topics/data.md>), [Orchestration](<https://devfeed.tech/topics/orchestration.md>), [Release notes](<https://devfeed.tech/topics/release-notes.md>), [SDK](<https://devfeed.tech/topics/sdk.md>), [Amazon S3](<https://devfeed.tech/topics/amazon-s3.md>), [BigQuery](<https://devfeed.tech/topics/bigquery.md>)

Tags: [airflow](<https://devfeed.tech/tags/airflow.md>), [apache](<https://devfeed.tech/tags/apache.md>), [apache-airflow](<https://devfeed.tech/tags/apache-airflow.md>), [bigquery](<https://devfeed.tech/tags/bigquery.md>), [data](<https://devfeed.tech/tags/data.md>), [docker-image](<https://devfeed.tech/tags/docker-image.md>), [orchestration](<https://devfeed.tech/tags/orchestration.md>), [pypi](<https://devfeed.tech/tags/pypi.md>), [release](<https://devfeed.tech/tags/release.md>), [release-notes](<https://devfeed.tech/tags/release-notes.md>), [s3](<https://devfeed.tech/tags/s3.md>), [sdk](<https://devfeed.tech/tags/sdk.md>), [workflows](<https://devfeed.tech/tags/workflows.md>)

## AI overview

Apache Airflow 3.2.0 introduces asset partitioning for more granular data-aware scheduling, along with multi-team deployments, synchronous deadline alert callbacks, and continued Task SDK separation. Partition-aware workflows trigger downstream DAGs only when the relevant partition is updated.

## Source excerpt

We're proud to announce the release of Apache Airflow 3.2.0! Airflow 3.1 puts humans at the center of automated workflows. 3.2 brings that same precision to data: Asset partitioning for granular pipeline orchestration, multi-team deployments for enterprise scale, synchronous deadline alert callbacks, and continued progress toward full Task SDK separation. Details: 📦 PyPI: https://pypi.org/project/apache-airflow/3.2.0/ 📚 Docs: https://airflow.apache.org/docs/apache-airflow/3.2.0/ 🛠 Release Notes: https://airflow.apache.org/docs/apache-airflow/3.2.0/release_notes.html 🐳 Docker Image: docker pull apache/airflow:3.2.0 🚏 Constraints: https://github.com/apache/airflow/tree/constraints-3.2.0 🗂 Asset Partitioning (AIP-76): Only the Right Work Gets Triggered Asset partitioning has been one of the most requested additions to data-aware scheduling. If you work with date-partitioned S3 paths, Hive table partitions, BigQuery partitions, or really any partitioned data store, you've dealt with this: An upstream task updates one partition, and every downstream Dag fires regardless of which slice actually changed. It's wasteful, and for large deployments it creates real operational noise. Asset partitioning in 3.2 makes this granular. Downstream Dags trigger only when the specific partition they care about gets updated. It's the biggest change to data-aware scheduling since Assets were introduced, and it turns partition-driven orchestration into something Airflow handles natively rather than something you work around. Key Capabilities Partition-driven scheduling: Dags trigger on specific partition updates, not every asset change CronPartitionTimetable: Schedule Dags against partitions using cron expressions. Also available in the Task SDK Backfill for partitioned Dags: Backfill historical partitions without re-triggering everything downstream (#61464) Multi-asset partitions: A single Dag can listen for partitions across multiple assets, which matters when your downstream work depe