JH-A-Kim commented on code in PR #67994:
URL: https://github.com/apache/airflow/pull/67994#discussion_r3384160788


##########
airflow-core/docs/core-concepts/overview.rst:
##########
@@ -120,25 +124,24 @@ finally with more isolated security perimeters.
 
 The meaning of the different connection types in the diagrams below is as 
follows:
 
-* **brown solid lines** represent *Dag files* submission and synchronization
+* **brown solid lines** represent *Dag bundles* submission and synchronization
 * **blue solid lines** represent deploying and accessing *installed packages* 
and *plugins*
 * **black dashed lines** represent control flow of workers by the *scheduler* 
(via executor)
 * **black solid lines** represent accessing the UI to manage execution of the 
workflows
-* **red dashed lines** represent accessing the *metadata database* by all 
components
+* **red dashed lines** represent accessing the *metadata database*
 
 .. _overview-basic-airflow-architecture:
 
 ..
-  TODO AIP-66 / AIP-72: These example architectures and diagrams need to be 
updated to reflect AF3 changes
-  like bundles, required Dag processor, execution api, etc.
+  TODO AIP-72: These diagrams need to be updated to reflect AF3 changes like 
bundles, required Dag processor, execution api, etc.
 
 Basic Airflow deployment
 ........................
 
 This is the simplest deployment of Airflow, usually operated and managed on a 
single
 machine. Such a deployment usually uses the LocalExecutor, where the 
*scheduler* and the *workers* are in
-the same Python process and the *Dag files* are read directly from the local 
filesystem by the *scheduler*.
-The *webserver* runs on the same machine as the *scheduler*. There is no 
*triggerer* component, which
+the same Python process. The *Dag processor* runs on the same machine, reads 
Dag files from the *Dag bundle* and serializes them into the *metadata database*
+for the *scheduler* to read. The *API server* runs on the same machine as the 
*scheduler*. There is no *triggerer* component, which
 means that task deferral is not possible.

Review Comment:
   the new commit b2253b6df44f9d741a663014ff294e554a6e0f1e resolves this! Thank 
you!



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