apache · vincbeck · Apr 30, 2026 · Apr 30, 2026 · Apr 30, 2026
@@ -390,7 +390,7 @@ you can use :class:`~airflow.providers.amazon.aws.sensors.sagemaker.SageMakerPro
 Branch a DAG based on condition evaluation
 ==========================================
 
-To branch an Airflow DAG based on upstream task outputs you can use
+To branch an Airflow Dag based on upstream task outputs you can use
 :class:`~airflow.providers.amazon.aws.operators.sagemaker.SageMakerConditionOperator`.
 
 Simple usage with flat parameters (single condition):

@@ -29,14 +29,14 @@
 
 class S3DagBundle(BaseDagBundle):
     """
-    S3 DAG bundle - exposes a directory in S3 as a DAG bundle.
+    S3 Dag bundle - exposes a directory in S3 as a Dag bundle.
 
-    This allows Airflow to load DAGs directly from an S3 bucket.
+    This allows Airflow to load Dags directly from an S3 bucket.
 
     :param aws_conn_id: Airflow connection ID for AWS.  Defaults to AwsBaseHook.default_conn_name.
-    :param bucket_name: The name of the S3 bucket containing the DAG files.
-    :param prefix:  Optional subdirectory within the S3 bucket where the DAGs are stored.
-                    If None, DAGs are assumed to be at the root of the bucket (Optional).
+    :param bucket_name: The name of the S3 bucket containing the Dag files.
+    :param prefix:  Optional subdirectory within the S3 bucket where the Dags are stored.
+                    If None, Dags are assumed to be at the root of the bucket (Optional).
     """
 
     supports_versioning = False
@@ -53,7 +53,7 @@ def __init__(
         self.aws_conn_id = aws_conn_id
         self.bucket_name = bucket_name
         self.prefix = prefix
-        # Local path where S3 DAGs are downloaded
+        # Local path where S3 Dags are downloaded
         self.s3_dags_dir: Path = self.base_dir
 
         log = structlog.get_logger(__name__)
@@ -69,11 +69,11 @@ def __init__(
     def _initialize(self):
         with self.lock():
             if not self.s3_dags_dir.exists():
-                self._log.info("Creating local DAGs directory: %s", self.s3_dags_dir)
+                self._log.info("Creating local Dags directory: %s", self.s3_dags_dir)
                 os.makedirs(self.s3_dags_dir)
 
             if not self.s3_dags_dir.is_dir():
-                raise AirflowException(f"Local DAGs path: {self.s3_dags_dir} is not a directory.")
+                raise AirflowException(f"Local Dags path: {self.s3_dags_dir} is not a directory.")
 
             if not self.s3_hook.check_for_bucket(bucket_name=self.bucket_name):
                 raise AirflowException(f"S3 bucket '{self.bucket_name}' does not exist.")
@@ -112,22 +112,22 @@ def __repr__(self):
         )
 
     def get_current_version(self) -> str | None:
-        """Return the current version of the DAG bundle. Currently not supported."""
+        """Return the current version of the Dag bundle. Currently not supported."""
         return None
 
     @property
     def path(self) -> Path:
-        """Return the local path to the DAG files."""
-        return self.s3_dags_dir  # Path where DAGs are downloaded.
+        """Return the local path to the Dag files."""
+        return self.s3_dags_dir  # Path where Dags are downloaded.
 
     def refresh(self) -> None:
-        """Refresh the DAG bundle by re-downloading the DAGs from S3."""
+        """Refresh the Dag bundle by re-downloading the Dags from S3."""
         if self.version:
             raise AirflowException("Refreshing a specific version is not supported")
 
         with self.lock():
             self._log.debug(
-                "Downloading DAGs from s3://%s/%s to %s", self.bucket_name, self.prefix, self.s3_dags_dir
+                "Downloading Dags from s3://%s/%s to %s", self.bucket_name, self.prefix, self.s3_dags_dir
             )
             self.s3_hook.sync_to_local_dir(
                 bucket_name=self.bucket_name,
@@ -138,15 +138,15 @@ def refresh(self) -> None:
 
     def view_url(self, version: str | None = None) -> str | None:
         """
-        Return a URL for viewing the DAGs in S3. Currently, versioning is not supported.
+        Return a URL for viewing the Dags in S3. Currently, versioning is not supported.
 
         This method is deprecated and will be removed when the minimum supported Airflow version is 3.1.
         Use `view_url_template` instead.
         """
         return self.view_url_template()
 
     def view_url_template(self) -> str | None:
-        """Return a URL for viewing the DAGs in S3. Currently, versioning is not supported."""
+        """Return a URL for viewing the Dags in S3. Currently, versioning is not supported."""
         if self.version:
             raise AirflowException("S3 url with version is not supported")
         if hasattr(self, "_view_url_template") and self._view_url_template:

@@ -36,7 +36,7 @@ RUN curl "https://github.com/apache/airflow/main/scripts/docker/e
 COPY <<"EOF" /install_dags_entrypoint.sh
 #!/bin/bash
 
-echo "Downloading DAGs from S3 bucket"
+echo "Downloading Dags from S3 bucket"
 aws s3 sync "$S3_URI" "$CONTAINER_DAG_PATH"
 
 /home/airflow/entrypoint.sh "$@"
@@ -81,27 +81,27 @@ ENV AWS_DEFAULT_REGION=$aws_default_region
 ENV AWS_SESSION_TOKEN=$aws_session_token
 
 
-## Loading DAGs
-# This Dockerfile supports 2 ways to load DAGs onto the container.
-# One is to upload all the DAGs onto an S3 bucket, and then
+## Loading Dags
+# This Dockerfile supports 2 ways to load Dags onto the container.
+# One is to upload all the Dags onto an S3 bucket, and then
 # download them onto the container. The other is to copy a local folder with
-# the DAGs onto the container.
-# If you would like to use an alternative method of loading DAGs, feel free to make the
+# the Dags onto the container.
+# If you would like to use an alternative method of loading Dags, feel free to make the
 # necessary changes to this file.
 
 ARG host_dag_path=./dags
 ENV HOST_DAG_PATH=$host_dag_path
 ARG container_dag_path=/opt/airflow/dags
 ENV CONTAINER_DAG_PATH=$container_dag_path
-# Set host_dag_path to the path of the DAGs on the host
+# Set host_dag_path to the path of the Dags on the host
 # COPY --chown=airflow:root $HOST_DAG_PATH $CONTAINER_DAG_PATH
 
 
-# Use these arguments to load DAGs onto the container from S3
+# Use these arguments to load Dags onto the container from S3
 ARG s3_uri
 ENV S3_URI=$s3_uri
-# If using S3 bucket as source of DAGs, uncommenting the next ENTRYPOINT command will overwrite this one.
+# If using S3 bucket as source of Dags, uncommenting the next ENTRYPOINT command will overwrite this one.
 ENTRYPOINT ["/usr/bin/dumb-init", "--", "/home/airflow/entrypoint.sh"]
 
-# Uncomment the line if using S3 bucket as the source of DAGs
+# Uncomment the line if using S3 bucket as the source of Dags
 # ENTRYPOINT ["/usr/bin/dumb-init", "--", "/install_dags_entrypoint.sh"]
@@ -80,18 +80,18 @@ ENV REQUIREMENTS_PATH=$requirements_path
 #ENV AWS_DEFAULT_REGION=$aws_default_region
 #ENV AWS_SESSION_TOKEN=$aws_session_token
 
-## Loading DAGs
-# This Dockerfile supports 2 ways to load DAGs onto the container.
+## Loading Dags
+# This Dockerfile supports 2 ways to load Dags onto the container.
 # One is to download them from S3 at runtime during the Lambda app invocation. The other
 # is to copy the dags into the image at build time, this will make task execution
 # much faster, since the images will already be present but the image will need to be rebuilt
-# every time the DAGs are updated.
-# If you would like to use an alternative method of loading DAGs, feel free to make the
+# every time the Dags are updated.
+# If you would like to use an alternative method of loading Dags, feel free to make the
 # necessary changes to this file.
 
 ARG host_dag_path=./dags
 ENV HOST_DAG_PATH=$host_dag_path
-# Uncomment the line below to copy the DAGs from the host to the container.
+# Uncomment the line below to copy the Dags from the host to the container.
 # COPY $HOST_DAG_PATH $AIRFLOW__CORE__DAGS_FOLDER
 
 # Use these arguments to load DAGs at runtime. If you are using the provided Lambda function (app.py),

@@ -111,8 +111,8 @@ def get_queue_url():
 
 
 def fetch_dags_from_s3(s3_uri):
-    """Fetch DAGs from S3 and sync them to the local dags directory."""
-    log.info("Fetching DAGs from S3 URI: %s", s3_uri)
+    """Fetch Dags from S3 and sync them to the local dags directory."""
+    log.info("Fetching Dags from S3 URI: %s", s3_uri)
     # Use a named temporary directory for the local dags folder, only tmp is writeable in Lambda
     local_dags_dir = mkdtemp(prefix="airflow_dags_")
     log.info("Setting AIRFLOW__CORE__DAGS_FOLDER to: %s", local_dags_dir)

@@ -259,8 +259,8 @@ def _handle_failed_job(self, job):
         # if the job fails before the Airflow process on the container has started. These failures
         # can be caused by a Batch API failure, container misconfiguration etc.
         # If the container is able to start up and run the Airflow process, any failures after that
-        # (i.e. DAG failures) will not be marked as Failed by AWS Batch, because Batch on assumes
-        # responsibility for ensuring the process started. Failures in the DAG will be caught by
+        # (i.e. Dag failures) will not be marked as Failed by AWS Batch, because Batch on assumes
+        # responsibility for ensuring the process started. Failures in the Dag will be caught by
         # Airflow, which will be handled separately.
         job_info = self.active_workers.id_to_job_info[job.job_id]
         task_key = self.active_workers.id_to_key[job.job_id]

@@ -586,7 +586,7 @@ def _get_caller(self, target_function_name: str = "execute") -> str:
     @return_on_error("00000000-0000-0000-0000-000000000000")
     def _generate_dag_key() -> str:
         """
-        Generate a DAG key.
+        Generate a Dag key.
 
         The Object Identifier (OID) namespace is used to salt the dag_id value.
         That salted value is used to generate a SHA-1 hash which, by definition,

@@ -43,7 +43,7 @@
 
 class AppflowBaseOperator(AwsBaseOperator[AppflowHook]):
     """
-    Amazon AppFlow Base Operator class (not supposed to be used directly in DAGs).
+    Amazon AppFlow Base Operator class (not supposed to be used directly in Dags).
 
     :param source: The source name (Supported: salesforce, zendesk)
     :param flow_name: The flow name

@@ -42,7 +42,7 @@
 
 class ComprehendBaseOperator(AwsBaseOperator[ComprehendHook]):
     """
-    This is the base operator for Comprehend Service operators (not supposed to be used directly in DAGs).
+    This is the base operator for Comprehend Service operators (not supposed to be used directly in Dags).
 
     :param input_data_config: The input properties for a PII entities detection job. (templated)
     :param output_data_config: Provides `conﬁguration` parameters for the output of PII entity detection

@@ -1196,7 +1196,7 @@ class EmrServerlessStartJobOperator(AwsBaseOperator[EmrServerlessHook]):
         This implies waiting for completion. This mode requires aiobotocore module to be installed.
         (default: False, but can be overridden in config file by setting default_deferrable to True)
     :param enable_application_ui_links: If True, the operator will generate one-time links to EMR Serverless
-        application UIs. The generated links will allow any user with access to the DAG to see the Spark or
+        application UIs. The generated links will allow any user with access to the Dag to see the Spark or
         Tez UI or Spark stdout logs. Defaults to False.
     :param cancel_on_kill: If True, the EMR Serverless job will be cancelled when the task is killed
         while in deferrable mode. This ensures that orphan jobs are not left running in EMR Serverless

@@ -41,23 +41,23 @@ class MwaaTriggerDagRunOperator(AwsBaseOperator[MwaaHook]):
         :ref:`howto/operator:MwaaTriggerDagRunOperator`
 
     :param env_name: The MWAA environment name (templated)
-    :param trigger_dag_id: The ID of the DAG to be triggered (templated)
+    :param trigger_dag_id: The ID of the Dag to be triggered (templated)
     :param trigger_run_id: The Run ID. This together with trigger_dag_id are a unique key. (templated)
     :param logical_date: The logical date (previously called execution date). This is the time or interval
-        covered by this DAG run, according to the DAG definition. This together with trigger_dag_id are a
+        covered by this Dag run, according to the Dag definition. This together with trigger_dag_id are a
         unique key. This field is required if your environment is running with Airflow 3. (templated)
-    :param data_interval_start: The beginning of the interval the DAG run covers
-    :param data_interval_end: The end of the interval the DAG run covers
+    :param data_interval_start: The beginning of the interval the Dag run covers
+    :param data_interval_end: The end of the interval the Dag run covers
     :param conf: Additional configuration parameters. The value of this field can be set only when creating
         the object. (templated)
     :param note: Contains manually entered notes by the user about the DagRun. (templated)
     :param airflow_version: The Airflow major version the MWAA environment runs.
             This parameter is only used if the local web token method is used to call Airflow API. (templated)
 
-    :param wait_for_completion: Whether to wait for DAG run to stop. (default: False)
+    :param wait_for_completion: Whether to wait for Dag run to stop. (default: False)
     :param waiter_delay: Time in seconds to wait between status checks. (default: 120)
-    :param waiter_max_attempts: Maximum number of attempts to check for DAG run completion. (default: 720)
-    :param deferrable: If True, the operator will wait asynchronously for the DAG run to stop.
+    :param waiter_max_attempts: Maximum number of attempts to check for Dag run completion. (default: 720)
+    :param deferrable: If True, the operator will wait asynchronously for the Dag run to stop.
         This implies waiting for completion. This mode requires aiobotocore module to be installed.
         (default: False)
     :param aws_conn_id: The Airflow connection used for AWS credentials.
@@ -122,10 +122,10 @@ def __init__(
     def execute_complete(self, context: Context, event: dict[str, Any] | None = None) -> dict:
         validated_event = validate_execute_complete_event(event)
         if validated_event["status"] != "success":
-            raise AirflowException(f"DAG run failed: {validated_event}")
+            raise AirflowException(f"Dag run failed: {validated_event}")
 
         dag_run_id = validated_event["dag_run_id"]
-        self.log.info("DAG run %s of DAG %s completed", dag_run_id, self.trigger_dag_id)
+        self.log.info("Dag run %s of Dag %s completed", dag_run_id, self.trigger_dag_id)
         return self.hook.invoke_rest_api(
             env_name=self.env_name,
             path=f"/dags/{self.trigger_dag_id}/dagRuns/{dag_run_id}",
@@ -157,9 +157,9 @@ def execute(self, context: Context) -> dict:
         )
 
         dag_run_id = response["RestApiResponse"]["dag_run_id"]
-        self.log.info("DAG run %s of DAG %s created", dag_run_id, self.trigger_dag_id)
+        self.log.info("Dag run %s of Dag %s created", dag_run_id, self.trigger_dag_id)
 
-        task_description = f"DAG run {dag_run_id} of DAG {self.trigger_dag_id} to complete"
+        task_description = f"Dag run {dag_run_id} of Dag {self.trigger_dag_id} to complete"
         if self.deferrable:
             self.log.info("Deferring for %s", task_description)
             self.defer(

@@ -2101,8 +2101,8 @@ def _cast(value: Any) -> Any:
         Cast Jinja-rendered string values to appropriate Python types.
 
         This is a compatibility shim for environments where
-        ``render_template_as_native_obj=True`` is not available at the DAG or
-        task level (e.g., YAML DAGs). Once task-level native rendering
+        ``render_template_as_native_obj=True`` is not available at the Dag or
+        task level (e.g., YAML Dags). Once task-level native rendering
         is widely supported, this method can be removed in favor of letting
         Airflow handle the casting natively.