Github user tdas commented on a diff in the pull request:
https://github.com/apache/spark/pull/18065#discussion_r118196588
--- Diff: docs/structured-streaming-programming-guide.md ---
@@ -10,7 +10,7 @@ title: Structured Streaming Programming Guide
# Overview
Structured Streaming is a scalable and fault-tolerant stream processing
engine built on the Spark SQL engine. You can express your streaming
computation the same way you would express a batch computation on static data.
The Spark SQL engine will take care of running it incrementally and
continuously and updating the final result as streaming data continues to
arrive. You can use the [Dataset/DataFrame API](sql-programming-guide.html) in
Scala, Java, Python or R to express streaming aggregations, event-time windows,
stream-to-batch joins, etc. The computation is executed on the same optimized
Spark SQL engine. Finally, the system ensures end-to-end exactly-once
fault-tolerance guarantees through checkpointing and Write Ahead Logs. In
short, *Structured Streaming provides fast, scalable, fault-tolerant,
end-to-end exactly-once stream processing without the user having to reason
about streaming.*
--- End diff --
The main title still says Experimental :P
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