diff --git a/dags/configuration.py b/dags/configuration.py
new file mode 100644
index 0000000..1f093f0
--- /dev/null
+++ b/dags/configuration.py
@@ -0,0 +1,85 @@
+"""
+Module containing DAG for configuration data
+
+Outputs:
+  - configuration data in parquet_datasets bucket
+  - configuration data in digital land db
+"""
+
+from datetime import timedelta
+
+import boto3
+from airflow import DAG
+from airflow.models.param import Param
+from airflow.operators.python import PythonOperator
+from airflow.providers.amazon.aws.operators.emr import EmrServerlessStartJobOperator
+from emr_dags_utils import get_secrets
+from utils import dag_default_args, get_config
+
+config = get_config()
+
+with DAG(
+    dag_id="configuration",
+    default_args=dag_default_args,
+    description="run processes related to our configuration files",
+    schedule=None,
+    catchup=False,
+    params={
+        "debug": Param(default=False, type="boolean", description="Enable debug logging for the Spark job"),
+    },
+    render_template_as_native_obj=True,
+    is_paused_upon_creation=False,
+) as dag:
+
+    ENV = config["env"]
+    EXECUTION_ROLE_ARN = get_secrets("emr_execution_role", ENV)
+    S3_BUCKET = f"{ENV}-pd-batch-jobs-codepackage-bucket"
+    S3_LOG_BUCKET = f"{ENV}-pd-batch-jobs-logs-bucket"
+    S3_TASKS_ENTRY_POINT = f"s3://{S3_BUCKET}/pkg/entry_script/run_config.py"
+    S3_WHEEL_FILE = f"s3://{S3_BUCKET}/pkg/whl_pkg/pyspark_jobs-0.1.0-py3-none-any.whl"
+    S3_LOG_URI = f"s3://{S3_LOG_BUCKET}/"
+    S3_DATA_PATH = f"s3://{ENV}-collection-data/"
+
+    def get_tasks_emr_application_id(**context):
+        app_name = f"{ENV}-pd-batch-emrsl-application"
+        client = boto3.client("emr-serverless", region_name="eu-west-2")
+        response = client.list_applications(maxResults=50)
+        for app in response.get("applications", []):
+            if app["name"] == app_name:
+                app_id = app["id"]
+                context["ti"].xcom_push(key="application_id", value=app_id)
+                return app_id
+        raise ValueError(f"EMR application '{app_name}' not found")
+
+    get_tasks_app_id = PythonOperator(
+        task_id="get-tasks-emr-app-id",
+        python_callable=get_tasks_emr_application_id,
+        execution_timeout=timedelta(minutes=2),
+        dag=dag,
+    )
+
+    assemble_tasks_emr_task = EmrServerlessStartJobOperator(
+        task_id="assemble-tasks",
+        application_id='{{ task_instance.xcom_pull(task_ids="get-tasks-emr-app-id", key="application_id") }}',
+        execution_role_arn=EXECUTION_ROLE_ARN,
+        job_driver={
+            "sparkSubmit": {
+                "entryPoint": S3_TASKS_ENTRY_POINT,
+                "entryPointArguments": (
+                    f"{{{{ ['--env', '{ENV}', '--collection-data-path', '{S3_DATA_PATH}', "
+                    f"'--parquet-datasets-path', 's3://{ENV}-parquet-datasets'] "
+                    f"+ (['--debug'] if params.debug else []) }}}}"
+                ),
+                "sparkSubmitParameters": f"--py-files {S3_WHEEL_FILE} " "--conf spark.serializer=org.apache.spark.serializer.KryoSerializer",
+            }
+        },
+        configuration_overrides={"monitoringConfiguration": {"s3MonitoringConfiguration": {"logUri": S3_LOG_URI}}},
+        name="assemble-tasks-job",
+        wait_for_completion=True,
+        aws_conn_id="aws_default",
+        waiter_max_attempts=180,
+        waiter_delay=60,
+        execution_timeout=timedelta(hours=3),
+    )
+
+    get_tasks_app_id >> assemble_tasks_emr_task