YngwieWang commented on a change in pull request #9350: [FLINK-13485]
[chinese-translation] Translate "Table API Example Walkthrough" page into
Chinese
URL: https://github.com/apache/flink/pull/9350#discussion_r310365456
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File path: docs/getting-started/walkthroughs/table_api.zh.md
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@@ -24,35 +24,39 @@ specific language governing permissions and limitations
under the License.
-->
-Apache Flink offers a Table API as a unified, relational API for batch and
stream processing, i.e., queries are executed with the same semantics on
unbounded, real-time streams or bounded, batch data sets and produce the same
results.
-The Table API in Flink is commonly used to ease the definition of data
analytics, data pipelining, and ETL applications.
+Apache Filnk为批流一体化提供了一种统一的、关系型API，即Tabel API。
+也就是说通过Tabel API建立的查询，在无界的实时数据流亦或是有界的批数据上具有同样的语义，得出的结果也是一样的。
+
+在Flink中Tabel API被广泛用于简化数据分析、数据工作流（data pipelining）和ETL应用程序的定义。
* This will be replaced by the TOC
{:toc}
-## What Will You Be Building?
+## 接下来你会构建什么？
+
+在这个教程中，你将会学习如何构建一个持续不断的ETL数据流，这个数据流会被用来按时间顺序追踪每个账户的财务交易。
-In this tutorial, you will learn how to build a continuous ETL pipeline for
tracking financial transactions by account over time.
-You will start by building your report as a nightly batch job, and then
migrate to a streaming pipeline.
+首先你会构建一个每晚运行的批作业，之后再把这个批作业转换成流作业。
-## Prerequisites
+## 预备知识
-This walkthrough assumes that you have some familiarity with Java or Scala,
but you should be able to follow along even if you are coming from a different
programming language.
-It also assumes that you are familiar with basic relational concepts such as
`SELECT` and `GROUP BY` clauses.
+这个示例假设你对Java和Scala有一定的了解，不过如果你习惯用其他编程语言，相信也是可以学会的。
Review comment:
```suggestion
本演练假设你对 Java 和 Scala 有一定的了解，但即便你使用其他编程语言，相信也可以学会。
```
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