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new 8db525eb96c [v3-1-test] Update README.md "DAG" to "Dag" for
consistency (#59554) (#59560)
8db525eb96c is described below
commit 8db525eb96c979eabf1c5d8b3ef63c47a16a4400
Author: github-actions[bot]
<41898282+github-actions[bot]@users.noreply.github.com>
AuthorDate: Wed Dec 17 17:16:31 2025 +0100
[v3-1-test] Update README.md "DAG" to "Dag" for consistency (#59554)
(#59560)
(cherry picked from commit 875a427ab3f1cc06bf470a9cebdd27aa8d9528a5)
Co-authored-by: Yeonguk Choo <[email protected]>
---
README.md | 18 +++++++++---------
generated/PYPI_README.md | 2 +-
2 files changed, 10 insertions(+), 10 deletions(-)
diff --git a/README.md b/README.md
index c92e9c496da..3e5b966d86e 100644
--- a/README.md
+++ b/README.md
@@ -48,7 +48,7 @@
When workflows are defined as code, they become more maintainable,
versionable, testable, and collaborative.
-Use Airflow to author workflows (Dags) that orchestrate tasks. The Airflow
scheduler executes your tasks on an array of workers while following the
specified dependencies. Rich command line utilities make performing complex
surgeries on DAGs a snap. The rich user interface makes it easy to visualize
pipelines running in production, monitor progress, and troubleshoot issues when
needed.
+Use Airflow to author workflows (Dags) that orchestrate tasks. The Airflow
scheduler executes your tasks on an array of workers while following the
specified dependencies. Rich command line utilities make performing complex
surgeries on Dags a snap. The rich user interface makes it easy to visualize
pipelines running in production, monitor progress, and troubleshoot issues when
needed.
<!-- END Apache Airflow, please keep comment here to allow auto update of PyPI
readme.md -->
<!-- START doctoc generated TOC please keep comment here to allow auto update
-->
@@ -82,7 +82,7 @@ Use Airflow to author workflows (Dags) that orchestrate
tasks. The Airflow sched
## Project Focus
-Airflow works best with workflows that are mostly static and slowly changing.
When the DAG structure is similar from one run to the next, it clarifies the
unit of work and continuity. Other similar projects include
[Luigi](https://github.com/spotify/luigi), [Oozie](https://oozie.apache.org/)
and [Azkaban](https://azkaban.github.io/).
+Airflow works best with workflows that are mostly static and slowly changing.
When the Dag structure is similar from one run to the next, it clarifies the
unit of work and continuity. Other similar projects include
[Luigi](https://github.com/spotify/luigi), [Oozie](https://oozie.apache.org/)
and [Azkaban](https://azkaban.github.io/).
Airflow is commonly used to process data, but has the opinion that tasks
should ideally be idempotent (i.e., results of the task will be the same, and
will not create duplicated data in a destination system), and should not pass
large quantities of data from one task to the next (though tasks can pass
metadata using Airflow's [XCom
feature](https://airflow.apache.org/docs/apache-airflow/stable/concepts/xcoms.html)).
For high-volume, data-intensive tasks, a best practice is to delegate to [...]
@@ -236,19 +236,19 @@ following the ASF Policy.
## User Interface
-- **DAGs**: Overview of all DAGs in your environment.
+- **Dags**: Overview of all Dags in your environment.
-
+
- **Assets**: Overview of Assets with dependencies.
-- **Grid**: Grid representation of a DAG that spans across time.
+- **Grid**: Grid representation of a Dag that spans across time.
-- **Graph**: Visualization of a DAG's dependencies and their current status
for a specific run.
+- **Graph**: Visualization of a Dag's dependencies and their current status
for a specific run.
@@ -256,11 +256,11 @@ following the ASF Policy.
-- **Backfill**: Backfilling a DAG for a specific date range.
+- **Backfill**: Backfilling a Dag for a specific date range.
-- **Code**: Quick way to view source code of a DAG.
+- **Code**: Quick way to view source code of a Dag.
@@ -367,7 +367,7 @@ this image in the `main` branch.
Airflow has a lot of dependencies - direct and transitive, also Airflow is
both - library and application,
therefore our policies to dependencies has to include both - stability of
installation of application,
-but also ability to install newer version of dependencies for those users who
develop DAGs. We developed
+but also ability to install newer version of dependencies for those users who
develop Dags. We developed
the approach where `constraints` are used to make sure airflow can be
installed in a repeatable way, while
we do not limit our users to upgrade most of the dependencies. As a result we
decided not to upper-bound
version of Airflow dependencies by default, unless we have good reasons to
believe upper-bounding them is
diff --git a/generated/PYPI_README.md b/generated/PYPI_README.md
index c1cc8739dc6..e97ee982224 100644
--- a/generated/PYPI_README.md
+++ b/generated/PYPI_README.md
@@ -50,7 +50,7 @@ PROJECT BY THE `generate-pypi-readme` PREK HOOK. YOUR CHANGES
HERE WILL BE AUTOM
When workflows are defined as code, they become more maintainable,
versionable, testable, and collaborative.
-Use Airflow to author workflows (Dags) that orchestrate tasks. The Airflow
scheduler executes your tasks on an array of workers while following the
specified dependencies. Rich command line utilities make performing complex
surgeries on DAGs a snap. The rich user interface makes it easy to visualize
pipelines running in production, monitor progress, and troubleshoot issues when
needed.
+Use Airflow to author workflows (Dags) that orchestrate tasks. The Airflow
scheduler executes your tasks on an array of workers while following the
specified dependencies. Rich command line utilities make performing complex
surgeries on Dags a snap. The rich user interface makes it easy to visualize
pipelines running in production, monitor progress, and troubleshoot issues when
needed.
## Requirements
