diff --git a/docs/reference/offline-stores/ray.md b/docs/reference/offline-stores/ray.md
index 89040f05c94..063ffcb209a 100644
--- a/docs/reference/offline-stores/ray.md
+++ b/docs/reference/offline-stores/ray.md
@@ -27,7 +27,7 @@ The template provides a complete working example with sample datasets and demons
 The Ray offline store provides:
 - Ray-based data reading from file sources (Parquet, CSV, etc.)
 - Support for local, remote, and KubeRay (Kubernetes-managed) clusters
-- Integration with various storage backends (local files, S3, GCS, HDFS)
+- Integration with various storage backends (local files, S3, GCS, HDFS, Azure Blob)
 - Efficient data filtering and column selection
 - Timestamp-based data processing with timezone awareness
 - Enterprise-ready KubeRay cluster support via CodeFlare SDK
@@ -463,6 +463,11 @@ job.persist(gcs_storage, allow_overwrite=True)
 # HDFS
 hdfs_storage = SavedDatasetFileStorage(path="hdfs://namenode:8020/datasets/driver_features.parquet")
 job.persist(hdfs_storage, allow_overwrite=True)
+
+# Azure Blob Storage / Azure Data Lake Storage Gen2
+# By setting AZURE_STORAGE_ANON=False it uses DefaultAzureCredential
+az_storage = SavedDatasetFileStorage(path="abfss://container@stc_account.dfs.core.windows.net/datasets/driver_features.parquet")
+job.persist(az_storage, allow_overwrite=True)
 ```
 
 ### Using Ray Cluster
diff --git a/sdk/python/feast/infra/compute_engines/ray/job.py b/sdk/python/feast/infra/compute_engines/ray/job.py
index 06eea4e5d88..e818220baff 100644
--- a/sdk/python/feast/infra/compute_engines/ray/job.py
+++ b/sdk/python/feast/infra/compute_engines/ray/job.py
@@ -18,6 +18,9 @@
 from feast.infra.compute_engines.dag.model import DAGFormat
 from feast.infra.compute_engines.dag.plan import ExecutionPlan
 from feast.infra.compute_engines.dag.value import DAGValue
+from feast.infra.offline_stores.contrib.ray_offline_store.ray import (
+    REMOTE_STORAGE_SCHEMES,
+)
 from feast.infra.offline_stores.file_source import SavedDatasetFileStorage
 from feast.infra.offline_stores.offline_store import RetrievalJob, RetrievalMetadata
 from feast.infra.ray_initializer import get_ray_wrapper
@@ -205,7 +208,7 @@ def persist(
         destination_path = storage.file_options.uri
 
         # Check if destination already exists
-        if not destination_path.startswith(("s3://", "gs://", "hdfs://")):
+        if not destination_path.startswith(REMOTE_STORAGE_SCHEMES):
             import os
 
             if not allow_overwrite and os.path.exists(destination_path):
diff --git a/sdk/python/feast/infra/offline_stores/contrib/ray_offline_store/ray.py b/sdk/python/feast/infra/offline_stores/contrib/ray_offline_store/ray.py
index b7b3619c15a..bc7c60733b4 100644
--- a/sdk/python/feast/infra/offline_stores/contrib/ray_offline_store/ray.py
+++ b/sdk/python/feast/infra/offline_stores/contrib/ray_offline_store/ray.py
@@ -62,6 +62,12 @@
 from feast.utils import _get_column_names, make_df_tzaware, make_tzaware
 
 logger = logging.getLogger(__name__)
+# Remote storage URI schemes supported by the Ray offline store
+# S3: Amazon S3
+# GCS: Google Cloud Storage
+# HDFS: Hadoop Distributed File System
+# Azure: Azure Storage Gen2
+REMOTE_STORAGE_SCHEMES = ("s3://", "gs://", "hdfs://", "abfs://", "abfss://")
 
 
 def _get_data_schema_info(
@@ -1160,13 +1166,13 @@ def persist(
                 f"Ray offline store only supports SavedDatasetFileStorage, got {type(storage)}"
             )
         destination_path = storage.file_options.uri
-        if not destination_path.startswith(("s3://", "gs://", "hdfs://")):
+        if not destination_path.startswith(REMOTE_STORAGE_SCHEMES):
             if not allow_overwrite and os.path.exists(destination_path):
                 raise SavedDatasetLocationAlreadyExists(location=destination_path)
         try:
             ray_ds = self._get_ray_dataset()
 
-            if not destination_path.startswith(("s3://", "gs://", "hdfs://")):
+            if not destination_path.startswith(REMOTE_STORAGE_SCHEMES):
                 os.makedirs(os.path.dirname(destination_path), exist_ok=True)
 
             ray_ds.write_parquet(destination_path)
@@ -1956,12 +1962,19 @@ def normalize_timestamps(batch: pd.DataFrame) -> pd.DataFrame:
             filesystem, resolved_path = FileSource.create_filesystem_and_path(
                 absolute_path, destination.s3_endpoint_override
             )
-            path_obj = Path(resolved_path)
-            if path_obj.suffix == ".parquet":
-                path_obj = path_obj.with_suffix("")
-            if not absolute_path.startswith(("s3://", "gs://")):
+            if absolute_path.startswith(REMOTE_STORAGE_SCHEMES):
+                write_path = (
+                    absolute_path[:-8]
+                    if absolute_path.endswith(".parquet")
+                    else absolute_path
+                )
+            else:
+                path_obj = Path(resolved_path)
+                if path_obj.suffix == ".parquet":
+                    path_obj = path_obj.with_suffix("")
                 path_obj.mkdir(parents=True, exist_ok=True)
-            ds.write_parquet(str(path_obj))
+                write_path = str(path_obj)
+            ds.write_parquet(write_path)
         except Exception as e:
             raise RuntimeError(f"Failed to write logged features: {e}")