yangyichao-mango commented on a change in pull request #12311:
URL: https://github.com/apache/flink/pull/12311#discussion_r432478582
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File path: docs/training/index.zh.md
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@@ -29,158 +29,90 @@ under the License.
* This will be replaced by the TOC
{:toc}
-## Goals and Scope of this Training
+## 本章教程的目标及涵盖范围
-This training presents 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 section will lead you to
where you
-can learn more.
+本章教程对 Apache Flink 的基本概念进行了介绍，虽然省略了许多重要细节，但是如果你掌握了本章内容，就足以实现可扩展并行度的
ETL、数据分析以及事件驱动的流式应用程序。本章重点对 Flink API
中的状态管理和时间进行了介绍，掌握了这些基础知识后，你将能更好地从其他详细参考文档中获取和掌握你所需要的知识。每小节结尾都有链接去引导你了解更多内容。
-Specifically, you will learn:
+具体来说，你将在本章学习到以下内容：
-- 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
+- 如何实现流数据处理管道（pipelines）
+- Flink 如何管理状态以及为何需要管理状态
+- 如何使用事件时间（event time）来一致并准确地进行计算分析
+- 如何在源源不断的数据流上构建事件驱动的应用程序
+- Flink 如何提供具有精确一次（exactly-once）计算语义的可容错、有状态流处理
-This training focuses on four critical concepts: continuous processing of
streaming data, event
-time, stateful stream processing, and state snapshots. This page introduces
these concepts.
+本章教程着重介绍四个概念：源源不断的流式数据处理、事件时间、有状态流处理和状态快照。基本概念介绍如下。
-{% info Note %} Accompanying this training is a set of hands-on exercises that
will
-guide you through learning how to work with the concepts being presented. A
link to the relevant
-exercise is provided at the end of each section.
+{% info Note %} 每小节教程都有实践练习部分去引导你如何在程序中使用其所述的概念，并在小节结尾都提供了相关实践练习的代码链接。
{% top %}
-## Stream Processing
+## 流处理
-Streams are data's natural habitat. Whether it is 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.
+在自然环境中，数据的产生原本就是流式的。无论是来自 Web
服务器的事件数据，证券交易所的交易数据，还是来自工厂车间机器上的传感器数据，其数据都是流式的进行生成。但是当你分析数据时，可以围绕
_有界流_（_bounded_）或 _无界流_（_unbounded_）两种模型来组织处理数据，当然，选择不同的模型，程序的执行和处理方式也都会不同。
Review comment:
> `其数据都是流式的进行生成` -> `其数据都是流式的` 会好一些吗？
> `也都会不同` -> `也会不同`
Thx.
`其数据都是流式的进行生成`我理解这样表达的是数据生成的方式是流式的
`其数据都是流式的`这样表达我理解`数据流式生成`的含义从字面意思会弱一些
读者应该都会理解，`其数据都是流式的`这样会更加流畅一些，我修改下。
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