diff --git a/Remote-Storage-Architecture.md b/Remote-Storage-Architecture.md
index 45a1c23..abddf85 100644
--- a/Remote-Storage-Architecture.md
+++ b/Remote-Storage-Architecture.md
@@ -67,6 +67,7 @@ The asynchronous write back will not slow down any local operations.
 
 * Machine learning training jobs need to repeatedly visit a large set of files. Increase training speed and reduce API cost and network cost.
 * Saving data files. With cloud capacity and storage tiering, saving data files there may be a good idea. The cache can save the programming effort.
+* Run Spark/Flink jobs on mounted folders for faster computation.
 * Multiple access methods, HDFS/HTTP/S3/WebDav/Mount, to access remote storage. No need to use one specific way to access remote storage.
 * If you plan to move off cloud, you can start with SeaweedFS Remote Storage Cache. When you are happy with it, just stop the write back process (and cancel the monthly payment to the cloud vendor!).