diff --git a/pipelines/datasets/br_anp_precos_combustiveis/flows.py b/pipelines/datasets/br_anp_precos_combustiveis/flows.py
index eaa5e4c96..6a003b91d 100644
--- a/pipelines/datasets/br_anp_precos_combustiveis/flows.py
+++ b/pipelines/datasets/br_anp_precos_combustiveis/flows.py
@@ -66,7 +66,6 @@
     with case(dados_desatualizados, True):
         df = download_and_transform(upstream_tasks=[rename_flow_run])
         output_path = make_partitions(df=df, upstream_tasks=[df])
-        get_date_max_pro = data_max_bd_pro(df=df)
 
         # pylint: disable=C0103
         wait_upload_table = create_table_and_upload_to_gcs(
@@ -91,6 +90,7 @@
                 },
                 labels=current_flow_labels,
                 run_name=f"Materialize {dataset_id}.{table_id}",
+                upstream_tasks=[wait_upload_table],
             )
 
             wait_for_materialization = wait_for_flow_run(
@@ -105,14 +105,16 @@
             wait_for_materialization.retry_delay = timedelta(
                 seconds=dump_db_constants.WAIT_FOR_MATERIALIZATION_RETRY_INTERVAL.value
             )
-
+            get_date_max_pro = data_max_bd_pro(
+                df=df, upstream_tasks=[wait_upload_table]
+            )
             with case(update_metadata, True):
                 update_django_metadata(
                     dataset_id,
                     table_id,
                     metadata_type="DateTimeRange",
                     bq_last_update=False,
-                    bq_table_last_year_month=True,
+                    bq_table_last_year_month=False,
                     api_mode="prod",
                     date_format="yy-mm-dd",
                     is_bd_pro=True,
@@ -120,7 +122,7 @@
                     time_delta=6,
                     time_unit="weeks",
                     _last_date=get_date_max_pro,
-                    upstream_tasks=[wait_upload_table],
+                    upstream_tasks=[get_date_max_pro],
                 )
 anp_microdados.storage = GCS(constants.GCS_FLOWS_BUCKET.value)
 anp_microdados.run_config = KubernetesRun(image=constants.DOCKER_IMAGE.value)
diff --git a/pipelines/datasets/br_anp_precos_combustiveis/tasks.py b/pipelines/datasets/br_anp_precos_combustiveis/tasks.py
index 46769d354..60f37a94a 100644
--- a/pipelines/datasets/br_anp_precos_combustiveis/tasks.py
+++ b/pipelines/datasets/br_anp_precos_combustiveis/tasks.py
@@ -40,9 +40,16 @@ def check_for_updates(dataset_id, table_id):
     # Obtém a data mais recente do site
     download_files(anatel_constants.URLS_DATA.value, anatel_constants.PATH_INPUT.value)
     df = pd.read_csv(anatel_constants.URL_GLP.value, sep=";", encoding="utf-8")
-    data_obj = pd.to_datetime(df["Data da Coleta"]).max()
-    data_obj = data_obj.date()
-
+    data_obj = (
+        df["Data da Coleta"].str[6:10]
+        + "-"
+        + df["Data da Coleta"].str[3:5]
+        + "-"
+        + df["Data da Coleta"].str[0:2]
+    )
+    data_obj = data_obj.apply(lambda x: pd.to_datetime(x).strftime("%Y-%m-%d"))
+    data_obj = pd.to_datetime(data_obj, format="%Y-%m-%d").dt.date
+    data_obj = data_obj.max()
     # Obtém a última data no site BD
     data_bq_obj = extract_last_date(
         dataset_id, table_id, "yy-mm-dd", "basedosdados", data="data_coleta"