[GitHub] [flink] alpinegizmo commented on a change in pull request #11826: [FLINK-17236][docs] Add Tutorials section overview

[GitBox <gi...@apache.org>]

alpinegizmo commented on a change in pull request #11826:
URL: https://github.com/apache/flink/pull/11826#discussion_r411656654



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File path: docs/tutorials/index.md
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@@ -0,0 +1,186 @@
+---
+title: Hands-on Tutorials
+nav-id: tutorials
+nav-pos: 2
+nav-title: '<i class="fa fa-hand-paper-o title appetizer" aria-hidden="true"></i> Hands-on Tutorials'
+nav-parent_id: root
+nav-show_overview: true
+always-expand: true
+---
+<!--
+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
+
+  http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing,
+software distributed under the License is distributed on an
+"AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+KIND, either express or implied.  See the License for the
+specific language governing permissions and limitations
+under the License.
+-->
+
+* This will be replaced by the TOC
+{:toc}
+
+## Goals and Scope of these Tutorials
+
+These tutorials present an introduction to Apache Flink that includes just enough to get you started
+writing scalable streaming ETL, analytics, and event-driven applications, while leaving out a lot of
+(ultimately important) details. The focus is on providing straightforward introductions to Flink's
+APIs for managing state and time, with the expectation that having mastered these fundamentals,
+you'll be much better equipped to pick up the rest of what you need to know from the more detailed
+reference documentation. The links at the end of each page will lead you to where you can learn
+more.
+
+Specifically, you will learn:
+
+- how to implement streaming data processing pipelines
+- how and why Flink manages state
+- how to use event time to consistently compute accurate analytics
+- how to build event-driven applications on continuous streams
+- how Flink is able to provide fault-tolerant, stateful stream processing with exactly-once semantics
+
+These tutorials focus on four critical concepts: continuous processing of streaming data, event
+time, stateful stream processing, and state snapshots. This page introduces these concepts.
+
+{% info Note %} Accompanying these tutorials are a set of hands-on exercises that will guide you
+through learning how to work with the concepts being presented.
+
+{% top %}
+
+## Stream Processing
+
+Streams are data's natural habitat. Whether it's events from web servers, trades from a stock
+exchange, or sensor readings from a machine on a factory floor, data is created as part of a stream.
+But when you analyze data, you can either organize your processing around _bounded_ or _unbounded_
+streams, and which of these paradigms you choose has profound consequences.
+
+<img src="{{ site.baseurl }}/fig/bounded-unbounded.png" alt="Bounded and unbounded streams" class="offset" width="90%" />
+
+**Batch processing** is the paradigm at work when you process a bounded data stream. In this mode of
+operation you can choose to ingest the entire dataset before producing any results, which means that
+it's possible, for example, to sort the data, compute global statistics, or produce a final report
+that summarizes all of the input.
+
+**Stream processing**, on the other hand, involves unbounded data streams. Conceptually, at least,
+the input may never end, and so you are forced to continuously process the data as it arrives. 
+
+In Flink, applications are composed of **streaming dataflows** that may be transformed by
+user-defined **operators**. These dataflows form directed graphs that start with one or more
+**sources**, and end in one or more **sinks**.
+
+<img src="{{ site.baseurl }}/fig/program_dataflow.svg" alt="A DataStream program, and its dataflow." class="offset" width="80%" />

Review comment:
       I fixed the bucketing sink rendering error in https://github.com/apache/flink/pull/11828#pullrequestreview-396512480 -- this figure is already in the docs, I'm just reusing it. I do agree about keyBy("id") though.




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