Bump apache-airflow from 2.10.5 to 3.2.0

[dependabot]

Bumps apache-airflow from 2.10.5 to 3.2.0.

Release notes

Sourced from apache-airflow's releases.

Apache Airflow 3.2.0

📦 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

Significant Changes

Asset Partitioning

The headline feature of Airflow 3.2.0 is asset partitioning — a major evolution of data-aware scheduling. Instead of triggering Dags based on an entire asset, you can now schedule downstream processing based on specific partitions of data. Only the relevant slice of data triggers downstream work, making pipeline orchestration far more efficient and precise.

This matters when working with partitioned data lakes — date-partitioned S3 paths, Hive table partitions, BigQuery table partitions, or any other partitioned data store. Previously, any update to an asset triggered all downstream Dags regardless of which partition changed. Now only the right work gets triggered at the right time.

For detailed usage instructions, see :doc:/authoring-and-scheduling/assets.

Multi-Team Deployments

Airflow 3.2 introduces multi-team support, allowing organizations to run multiple isolated teams within a single Airflow deployment. Each team can have its own Dags, connections, variables, pools, and executors— enabling true resource and permission isolation without requiring separate Airflow instances per team.

This is particularly valuable for platform teams that serve multiple data engineering or data science teams from shared infrastructure, while maintaining strong boundaries between teams' resources and access.

For detailed usage instructions, see :doc:/core-concepts/multi-team.

.. warning::

Multi-Team Deployments are experimental in 3.2.0 and may change in future versions based on user feedback.

Synchronous callback support for Deadline Alerts

Deadline Alerts now support synchronous callbacks via SyncCallback in addition to the existing asynchronous AsyncCallback. Synchronous callbacks are executed by the executor (rather than the triggerer), and can optionally target a specific executor via the executor parameter.

A Dag can also define multiple Deadline Alerts by passing a list to the deadline parameter, and each alert can use either callback type.

.. warning::

Deadline Alerts are experimental in 3.2.0 and may change in future versions based on

... (truncated)

Commits

• 3bc3ccf Update release notes for 3.2.0rc2
• 5311961 [v3-2-test] Guard against null trigger in asset watcher cleanup (#64659) (#64...
• 3840fae [v3-2-test] Fix ObjectStoragePath NoCredentialsError when using conn_id with ...
• 959ebd8 [v3-2-test] Fix double-serialization issue by unwrapping serialized kwargs in...
• d1d2416 [v3-2-test] Run DB check only for core components in prod entrypoint (#63413)...
• beee8b6 [v3-2-test] Remove false-positive RFC3986 underscore warning from Connection....
• 68d9874 [v3-2-test] fix: restore early return in check_for_write_conflict (#64062) (#...
• 0a000c4 [v3-2-test] Fix deferred task resume failure when worker is older than server...
• 8d0fb4b [v3-2-test] Fix serde deserialization of old-format builtin types in trigger ...
• e64b7e4 [v3-2-test] Fix: Restore live stdout logging for Elasticsearch log forwarding...
• Additional commits viewable in compare view

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