diff --git a/docs/how-to-guides/running-feast-in-production.md b/docs/how-to-guides/running-feast-in-production.md
index 53808326091..fa9f1a0193b 100644
--- a/docs/how-to-guides/running-feast-in-production.md
+++ b/docs/how-to-guides/running-feast-in-production.md
@@ -310,6 +310,30 @@ We use an [InfluxDB-style extension](https://github.com/prometheus/statsd_export
 We chose StatsD since it's a de-facto standard with various implementations (eg, [1](https://github.com/prometheus/statsd_exporter), [2](https://github.com/influxdata/telegraf/blob/master/plugins/inputs/statsd/README.md))
 and metrics can be easily exported to Prometheus, InfluxDB, AWS CloudWatch, etc.
 
+## 7. Using environment variables in your yaml configuration
+
+You might want to dynamically set parts of your configuration from your environment. For instance to deploy Feast to production and development with the same configuration, but a different server. Or to inject secrets without exposing them in your git repo. To do this, it is possible to use the `${ENV_VAR}` syntax in your `feature_store.yaml` file. For instance:
+
+```yaml
+project: my_project
+registry: data/registry.db
+provider: local
+online_store:
+    type: redis
+    connection_string: ${REDIS_CONNECTION_STRING}
+```
+
+It is possible to set a default value if the environment variable is not set, with `${ENV_VAR:"default"}`. For instance:
+
+```yaml
+project: my_project
+registry: data/registry.db
+provider: local
+online_store:
+    type: redis
+    connection_string: ${REDIS_CONNECTION_STRING:"0.0.0.0:6379"}
+```
+
 ---
 ## Summary
 
diff --git a/sdk/python/feast/infra/offline_stores/contrib/spark_offline_store/spark_source.py b/sdk/python/feast/infra/offline_stores/contrib/spark_offline_store/spark_source.py
index c94b1913598..daf3cd252ad 100644
--- a/sdk/python/feast/infra/offline_stores/contrib/spark_offline_store/spark_source.py
+++ b/sdk/python/feast/infra/offline_stores/contrib/spark_offline_store/spark_source.py
@@ -25,6 +25,8 @@ class SparkSourceFormat(Enum):
     csv = "csv"
     json = "json"
     parquet = "parquet"
+    delta = "delta"
+    avro = "avro"
 
 
 class SparkSource(DataSource):