knaufk commented on a change in pull request #11092: [FLINK-15999] Extract 
“Concepts” material from API/Library sections and start proper concepts section

 File path: docs/concepts/
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+title: Stateful Stream Processing
+nav-id: stateful-stream-processing
+nav-pos: 2
+nav-title: Stateful Stream Processing
+nav-parent_id: concepts
+Licensed to the Apache Software Foundation (ASF) under one
+or more contributor license agreements.  See the NOTICE file
+distributed with this work for additional information
+regarding copyright ownership.  The ASF licenses this file
+to you under the Apache License, Version 2.0 (the
+"License"); you may not use this file except in compliance
+with the License.  You may obtain a copy of the License at
+Unless required by applicable law or agreed to in writing,
+software distributed under the License is distributed on an
+KIND, either express or implied.  See the License for the
+specific language governing permissions and limitations
+under the License.
+While many operations in a dataflow simply look at one individual *event at a
+time* (for example an event parser), some operations remember information
+across multiple events (for example window operators).  These operations are
+called **stateful**.
+Stateful functions and operators store data across the processing of individual
+elements/events, making state a critical building block for any type of more
+elaborate operation.
+For example:
+  - When an application searches for certain event patterns, the state will
+    store the sequence of events encountered so far.
+  - When aggregating events per minute/hour/day, the state holds the pending
+    aggregates.
+  - When training a machine learning model over a stream of data points, the
+    state holds the current version of the model parameters.
+  - When historic data needs to be managed, the state allows efficient access
+    to events that occurred in the past.
+Flink needs to be aware of the state in order to make state fault tolerant
+using [checkpoints]({{ site.baseurl}}{% link dev/stream/state/
+%}) and to allow [savepoints]({{ site.baseurl }}{%link ops/state/
+%}) of streaming applications.
+Knowledge about the state also allows for rescaling Flink applications, meaning
+that Flink takes care of redistributing state across parallel instances.
+The [queryable state]({{ site.baseurl }}{% link
+dev/stream/state/ %}) feature of Flink allows you to access
+state from outside of Flink during runtime.
+When working with state, it might also be useful to read about [Flink's state
+backends]({{ site.baseurl }}{% link ops/state/ %}). Flink
+provides different state backends that specify how and where state is stored.
+State can be located on Java's heap or off-heap. Depending on your state
+backend, Flink can also *manage* the state for the application, meaning Flink
+deals with the memory management (possibly spilling to disk if necessary) to
+allow applications to hold very large state. State backends can be configured
+without changing your application logic.
+* This will be replaced by the TOC
+## What is State?
+`TODO: expand this section`
+There are different types of state in Flink, the most-used type of state is
+*Keyed State*. For special cases you can use *Operator State* and *Broadcast
+State*. *Broadcast State* is a special type of *Operator State*.
+{% top %}
+## State in Stream & Batch Processing
 Review comment:
   As far as I remember, this was supposed to be about the conceptual 
differences between state handling in batch and stream processing. I don't 
remember exactly, what we talked about here. The only thing I can think of 
right now is, that the data structures in which state is stored need to be 
different in batch and stream processing for it to be efficient.
   I guess one could also say something about boundedness and expiry of state?
   @StephanEwen what did we have in mind here?

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