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[airflow-site] 01/01: Add posts about 2.4 release

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commit 287b4f267d3c7d1b7258261c8227662626d5d4c9
Author: Ash Berlin-Taylor <as...@firemirror.com>
AuthorDate: Fri Sep 16 14:05:42 2022 +0100

    Add posts about 2.4 release
---
 .../site/content/en/announcements/_index.md        |   9 ++
 .../site/content/en/blog/airflow-2.4.0/index.md    | 140 +++++++++++++++++++++
 2 files changed, 149 insertions(+)

diff --git a/landing-pages/site/content/en/announcements/_index.md b/landing-pages/site/content/en/announcements/_index.md
index 91c263a6fb..45ae370313 100644
--- a/landing-pages/site/content/en/announcements/_index.md
+++ b/landing-pages/site/content/en/announcements/_index.md
@@ -13,6 +13,15 @@ menu:
 
 **Note:** Follow [@ApacheAirflow](https://twitter.com/ApacheAirflow) on Twitter for the latest news and announcements!
 
+# September 19, 2022
+
+We’ve just released Apache **Airflow 2.4.0**. You can read all about [what's new in Apache Airflow 2.4.0]({{< ref "blog/airflow-2.4.0/index.md" >}}) blog post.
+
+📦 PyPI: https://pypi.org/project/apache-airflow/2.4.0/ \
+📚 Docs: https://airflow.apache.org/docs/apache-airflow/2.4.0 \
+🛠️ Release Notes: https://airflow.apache.org/docs/apache-airflow/2.4.0/release_notes.html \
+🪶 Sources: https://airflow.apache.org/docs/apache-airflow/2.4.0/installation/installing-from-sources.html
+
 # August 23, 2022
 
 We’ve just released Apache **Airflow 2.3.4**.
diff --git a/landing-pages/site/content/en/blog/airflow-2.4.0/index.md b/landing-pages/site/content/en/blog/airflow-2.4.0/index.md
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@@ -0,0 +1,140 @@
+---
+title: "Apache Airflow 2.4.0: Data
+linkTitle: "Apache Airflow 2.4.0"
+author: "Ash Berlin-Taylor"
+github: "ashberlin"
+linkedin: "ashberlin-taylor"
+description: "We're proud to announce that Apache Airflow 2.4.0 has been released."
+tags: [Release]
+date: "2022-09-19"
+---
+
+Apache Airflow
+Apache Airflow 2.4.0 contains over 650 "user-facing" commits in this release (excluding commits to providers or chart) and over 870 in total, not cou since 2.3.0 and includes 50 new features, 99 improvements, 85 bug fixes, and several doc changes.
+
+**Details**:
+
+📦 PyPI: https://pypi.org/project/apache-airflow/2.4.0/ \
+📚 Docs: https://airflow.apache.org/docs/apache-airflow/2.4.0/ \
+🛠️ Release Notes: https://airflow.apache.org/docs/apache-airflow/2.4.0/release_notes.html \
+🐳 Docker Image: docker pull apache/airflow:2.4.0 \
+🚏 Constraints: https://github.com/apache/airflow/tree/constraints-2.4.0
+
+
+## Data-aware scheduling (AIP-48)
+
+This one is big. Airflow now has the ability to schedule DAGs based on other tasks updating datasets.
+
+What does this mean, exactly? This is a great new feature and lets DAG authors create smaller, more self-contained DAGs, that can chain together into a larger data-based workflow. If you are currently using `ExternalTaskSensor` or `TriggerDagRunOperator` you should take a look at datasets -- in most cases you can replace them with something that will speed up the scheduling!
+
+But enough talking, lets have a short example. First lets create the task that produces the dataset:
+
+```python
+from airflow import Dataset
+
+
+dataset = Dataset(uri='my-dataset')
+
+with DAG(dag_id='producer', ...)
+    @task(outlets=[dataset])
+    def my_task():
+        ...
+```
+
+And then we can tell Airflow to schedule a DAG whenever this Dataset changes:
+
+```python
+
+from airflow import Dataset
+
+
+dataset = Dataset(uri='my-dataset')
+
+with DAG(dag_id='dataset-consumer', schedule=[dataset]):
+    ...
+```
+
+With these two dags, the instant that the `my_task` finishes Airflow will create the DAG run for the `dataset-consumer` workflow.
+
+(If you have the produce and consumer in different files you do not need to use the same Dataset object, two `Dataset()`s created with the same URI are equal.)
+
+We know that what exists right now won't fit all use cases that people might wish for datasets, and in the coming minor releases (2.5, 2.6, etc.) we will expand and improve upon this foundation
+
+Datasets represent the abstract concept of a dataset, and (for now) do not have any direct read or write capability - in this release we are adding the foundational feature that we will build upon in future - and it's part of our goal to have smaller releases to get new features in your hands sooner!
+
+For more info on Datasets please see the [docs on Data-aware scheduling][data-aware-scheduling], including how datasets are identified (URIs), how you can depend on multiple datasets, and how to think about what is a Dataset (hint: don't include "date partitions" in a dataset, it's higher level than that.)
+
+[data-aware-scheduling]: https://airflow.apache.org/docs/apache-airflow/stable/concepts/datasets.html
+
+## More improvments to Dynamic Task Mapping (AIP-42)
+
+You asked, we listened. Dynamic task mapping now includes support for `expand_kwargs` (to assign multiple parameters to a non-TaskFlow operator), `zip` (to combine multiple things without cross-product) and `map` (to transform the parameters on just before the task is run).
+
+For more info on dynamic task mapping please the new sections of the doc on [Transforming Mapped Data][transforming-mapped-data], [Combining upstream data (aka "zipping")][task-mapping-zip], and [Assigning multiple parameters to a non-TaskFlow operator][expand-kwargs].
+
+[task-mapping-zip]: https://airflow.apache.org/docs/apache-airflow/stable/concepts/dynamic-task-mapping.html#combining-upstream-data-aka-zipping
+[transforming-mapped-data]: https://airflow.apache.org/docs/apache-airflow/stable/concepts/dynamic-task-mapping.html#transforming-mapped-data
+[expand-kwargs]: https://airflow.apache.org/docs/apache-airflow/stable/concepts/dynamic-task-mapping.html#assigning-multiple-parameters-to-a-non-taskflow-operator
+
+## Auto-register DAGS used in a context manager (no more `as dag:` needed)
+
+This one is a small quality of life improvement, and I don't want to admit how many times I forgot the `as dag:`, or worse, had `as dag:` repeated (meaning only the last use showed up)
+
+```python
+
+with DAG(dag_id="example") as dag:
+  ...
+
+
+@dag
+def dag_maker():
+    ...
+
+
+dag2 = dag_maker()
+
+can become
+
+```python
+
+with DAG(dag_id="example"):
+    ...
+
+
+@dag
+def my_dag():
+    ...
+
+
+my_dag()
+
+If you want to disable the behaviour for any reason then set `auto_register=False` on the dag:
+
+```python
+# This dag will not be picked up by Airflow as it's not assigned to a variable
+with DAG(dag_id="example", auto_register=False):
+    ...
+```
+
+## Removal of experimental Smart Sensors feature
+
+Smart Sensors were added in 2.0 and deprecated in favor of Deferrable operators in 2.2, and have now been removed. If you were using smart sensors you will have to switch to using deferrable operators before you can upgrade.
+
+We're sorry to remove this feature (and we didn't do it lightly) but to enable us to continue to grow and evolve Airflow we needed to remove this experimental code. We will only do this sort of change in a minor release for features marked as experimental, for anything that is fully supported it won't be removed or broken until the next major release.
+
+## Other small features
+
+With over 650 commits the full list of features, fixes and changes is too big to go in to here (check out the release notes for a full list), but some noteworthy or interesting small features include:
+
+- Auto-refresh on the home page
+- Add `@task.short_circuit` TaskFlow decorator
+- Add roles delete command to cli (#25854)
+- Add support for `TaskGroup` in `ExternalTaskSensor` (#24902)
+- Add `@task.kubernetes` taskflow decorator (#25663)
+- Consolidate to one `schedule` param (#25410)
+- Allow showing non-sensitive config values in Admin -> Configuration (rather than all or nothing) (#25346)
+- Operator name separate from class (no more `_PythonDecoratedOperator` when using TaskFlow) (#22834)
+
+## Contributors
+
+Thanks to everyone who contributed to this release, including Andrey Anshin, Ash Berlin-Taylor, Bartłomiej Hirsz, Brent Bovenzi, Chenglong Yan, D. Ferruzzi, Daniel Standish, Ephraim Anierobi, Jarek Potiuk, Jed Cunningham, Josh Fell, Mark Norman Francis, Niko, Tzu-ping Chung, Vincent, Wojciech Januszek, blag, chethanuk-plutoflume, eladkal, pierrejeambrun, and everyone else who commited, all 152 of you! You are what makes Airflow the successful project that is is!