diff --git a/ads/features/feature_tables/ad_embeddings.py b/ads/features/feature_tables/ad_embeddings.py
index 211abad..395a198 100644
--- a/ads/features/feature_tables/ad_embeddings.py
+++ b/ads/features/feature_tables/ad_embeddings.py
@@ -1,8 +1,9 @@
 from tecton.types import Field, String, Timestamp, Array, Float64
-from tecton import Entity, FeatureTable, DeltaConfig
-from ads.entities import ad
+from tecton import Entity, FeatureTable
 from datetime import timedelta
 
+ad = Entity(name='ad_embeddings_entity', join_keys=["ad_id"])
+
 schema = [
     Field('ad_id', String),
     Field('timestamp', Timestamp),
diff --git a/ads/features/stream_features/content_keyword_clicks_push.py b/ads/features/stream_features/content_keyword_clicks_push.py
index 69c8d7f..2ad6946 100644
--- a/ads/features/stream_features/content_keyword_clicks_push.py
+++ b/ads/features/stream_features/content_keyword_clicks_push.py
@@ -1,5 +1,5 @@
 from datetime import timedelta, datetime
-from tecton import StreamFeatureView, FilteredSource
+from tecton import StreamFeatureView, FilteredSource, BatchTriggerType
 from ads.entities import content_keyword
 from ads.data_sources.ad_impressions import keyword_click_source
 
@@ -19,5 +19,6 @@
     ttl=timedelta(days=30),
     tags={'release': 'production'},
     owner='demo-user@tecton.ai',
-    description='The ad clicks for a content keyword'
+    description='The ad clicks for a content keyword',
+    batch_trigger=BatchTriggerType.MANUAL
 )
diff --git a/repo.yaml b/repo.yaml
new file mode 100644
index 0000000..eabe9e1
--- /dev/null
+++ b/repo.yaml
@@ -0,0 +1,58 @@
+# This is the Tecton repo config. It's used to configure how Tecton builds and applies your
+# feature definitions during `tecton plan/apply/test`.
+#
+# By default, the Tecton CLI will use the Repo Config specified at <TECTON_REPO_ROOT>/repo.yaml, but you
+# can specify another file by using `tecton plan --config my_config.yaml`.
+
+# The `defaults` keyword specifies default parameter values for Tecton objects defined in your Feature Repository.
+# For example, you can set a default `tecton_materialization_runtime` for all Batch Feature Views.
+# Defaults can be overridden on a per-object basis in your Python feature definitions.
+# See Tecton's documentation for details on which Tecton objects are currently supported by the `defaults` keyword.
+
+defaults:
+  batch_feature_view:
+    tecton_materialization_runtime: 0.9.0
+    environment: tecton-rift-core-0.9.0  # For Rift-based Batch Feature Views
+  stream_feature_view:
+    tecton_materialization_runtime: 0.9.0
+    environment: tecton-rift-core-0.9.0  # For Rift-based Stream Feature Views
+  feature_table:
+    tecton_materialization_runtime: 0.9.0
+
+# Below is an example of other defaults that can be set using the `defaults` keyword.
+# defaults:
+#   batch_feature_view:
+#     tecton_materialization_runtime: 0.9.0
+#     online_store:
+#       kind: RedisConfig
+#     offline_store:
+#       kind: OfflineStoreConfig
+#       staging_table_format:
+#         kind: ParquetConfig
+#     batch_compute:
+#       kind: DatabricksClusterConfig
+#       instance_type: m5.xlarge
+#       number_of_workers: 2
+#       extra_pip_dependencies:
+#         - haversine==2.8.0
+#   stream_feature_view:
+#     tecton_materialization_runtime: 0.9.0
+#     stream_compute:
+#       kind: DatabricksClusterConfig
+#       instance_availability: on_demand
+#       instance_type: m5.2xlarge
+#       number_of_workers: 4
+#     offline_store:
+#       kind: OfflineStoreConfig
+#       staging_table_format:
+#         kind: ParquetConfig
+#   feature_table:
+#     tecton_materialization_runtime: 0.9.0
+#     batch_compute:
+#       kind: DatabricksClusterConfig
+#       instance_type: m5.xlarge
+#       number_of_workers: 2
+#     online_store:
+#       kind: RedisConfig
+#   feature_service:
+#     on_demand_environment: tecton-python-extended:0.4