diff --git a/metadata-ingestion/README.md b/metadata-ingestion/README.md
index 802a34a28b..bc49793780 100644
--- a/metadata-ingestion/README.md
+++ b/metadata-ingestion/README.md
@@ -2,6 +2,7 @@
 
 This module hosts an extensible Python-based metadata ingestion system for DataHub.
 This supports sending data to DataHub using Kafka or through the REST api.
+It can be used through our CLI tool or as a library e.g. with an orchestrator like Airflow.
 
 ## Architecture
 
@@ -62,6 +63,10 @@ source docker/docker_run.sh examples/recipes/file_to_file.yml
 ```
 -->
 
+We have also included a couple [sample DAGs](./examples/airflow) that can be used with [Airflow](https://airflow.apache.org/).
+- `generic_recipe_sample_dag.py` - a simple Airflow DAG that picks up a DataHub ingestion recipe configuration and runs it.
+- `mysql_sample_dag.py` - an Airflow DAG that runs a MySQL metadata ingestion pipeline using an inlined configuration.
+
 # Recipes
 
 A recipe is a configuration file that tells our ingestion scripts where to pull data from (source) and where to put it (sink).
diff --git a/metadata-ingestion/examples/airflow/generic_recipe_sample_dag.py b/metadata-ingestion/examples/airflow/generic_recipe_sample_dag.py
new file mode 100644
index 0000000000..7bb37d15ae
--- /dev/null
+++ b/metadata-ingestion/examples/airflow/generic_recipe_sample_dag.py
@@ -0,0 +1,50 @@
+"""Generic DataHub Ingest via Recipe
+
+This example demonstrates how to load any configuration file and run a
+DataHub ingestion pipeline within an Airflow DAG.
+"""
+
+from datetime import timedelta
+
+import yaml
+
+from airflow import DAG
+from airflow.utils.dates import days_ago
+from airflow.operators.python import PythonOperator
+
+from datahub.ingestion.run.pipeline import Pipeline
+
+
+default_args = {
+    "owner": "airflow",
+    "depends_on_past": False,
+    "email": ["jdoe@example.com"],
+    "email_on_failure": False,
+    "email_on_retry": False,
+    "retries": 1,
+    "retry_delay": timedelta(minutes=5),
+    "execution_timeout": timedelta(minutes=120),
+}
+
+
+def datahub_recipe():
+    with open("path/to/recipe.yml") as config_file:
+        config = yaml.safe_load(config_file)
+
+    pipeline = Pipeline.create(config)
+    pipeline.run()
+
+
+with DAG(
+    "datahub_ingest_using_recipe",
+    default_args=default_args,
+    description="An example DAG which runs a DataHub ingestion recipe",
+    schedule_interval=timedelta(days=1),
+    start_date=days_ago(2),
+    tags=["datahub-ingest"],
+    catchup=False,
+) as dag:
+    ingest_task = PythonOperator(
+        task_id="ingest_using_recipe",
+        python_callable=datahub_recipe,
+    )
diff --git a/metadata-ingestion/examples/airflow/mysql_sample_dag.py b/metadata-ingestion/examples/airflow/mysql_sample_dag.py
new file mode 100644
index 0000000000..98f7eeca34
--- /dev/null
+++ b/metadata-ingestion/examples/airflow/mysql_sample_dag.py
@@ -0,0 +1,62 @@
+"""MySQL DataHub Ingest DAG
+
+This example demonstrates how to ingest metadata from MySQL into DataHub
+from within an Airflow DAG. Note that the DB connection configuration is
+embedded within the code.
+"""
+
+from datetime import timedelta
+
+from airflow import DAG
+from airflow.utils.dates import days_ago
+from airflow.operators.python import PythonOperator
+
+from datahub.ingestion.run.pipeline import Pipeline
+
+
+default_args = {
+    "owner": "airflow",
+    "depends_on_past": False,
+    "email": ["jdoe@example.com"],
+    "email_on_failure": False,
+    "email_on_retry": False,
+    "retries": 1,
+    "retry_delay": timedelta(minutes=5),
+    "execution_timeout": timedelta(minutes=120),
+}
+
+
+def ingest_from_mysql():
+    pipeline = Pipeline.create(
+        {
+            "source": {
+                "type": "mysql",
+                "config": {
+                    "username": "user",
+                    "password": "pass",
+                    "database": "db_name",
+                    "host_port": "localhost:3306",
+                },
+            },
+            "sink": {
+                "type": "datahub-rest",
+                "config": {"server": "http://localhost:8080"},
+            },
+        }
+    )
+    pipeline.run()
+
+
+with DAG(
+    "datahub_mysql_ingest",
+    default_args=default_args,
+    description="An example DAG which ingests metadata from MySQL to DataHub",
+    schedule_interval=timedelta(days=1),
+    start_date=days_ago(2),
+    tags=["datahub-ingest"],
+    catchup=False,
+) as dag:
+    ingest_task = PythonOperator(
+        task_id="ingest_from_mysql",
+        python_callable=ingest_from_mysql,
+    )