[doc] Modify features, hw, intro and homepage docs. (#10713)

docs/configs/docsdev.js

@@ -27,6 +27,10 @@ export default {
                         title: 'Introduction',
                         link: '/en-us/docs/dev/user_doc/about/introduction.html',
                     },
+                    {
+                        title: 'Features',
+                        link: '/en-us/docs/dev/user_doc/about/feature.html',
+                    },
                     {
                         title: 'Hardware Environment',
                         link: '/en-us/docs/dev/user_doc/about/hardware.html',
@@ -34,7 +38,7 @@ export default {
                     {
                         title: 'Glossary',
                         link: '/en-us/docs/dev/user_doc/about/glossary.html',
-                    },
+                    }
                 ],
             },
             {
@@ -423,6 +427,10 @@ export default {
                         title: '简介',
                         link: '/zh-cn/docs/dev/user_doc/about/introduction.html',
                     },
+                    {
+                        title: '特性',
+                        link: '/zh-cn/docs/dev/user_doc/about/feature.html',
+                    },
                     {
                         title: '建议配置',
                         link: '/zh-cn/docs/dev/user_doc/about/hardware.html',

docs/docs/en/about/features.md

+# Features
+
+## Simple to Use
+
+- **Visual DAG**: User-friendly drag-and-drop workflow definition and facility for run-time control.
+- **Modular Operation**: Modularity facilitates easy customization and maintenance.
+
+## Rich Scenarios
+
+- **Multiple Task Type Support**: Supports more than 10 task types, like Shell, MR, Spark, SQL, etc., with cross-language support making it easy to extend
+- **Workflow Ops**: Workflow can be timed, paused, resumed and stopped, enabling easy maintenance and control of global and local parameters.
+
+## High Reliability
+
+- **Reliability**: Decentralized designs ensure stability. Self-supporting HA task queue to avoid overload fault tolerant capability. DolphinScheduler facilitates a highly robust environment.
+
+## High Scalability
+
+- **Scalability**: Supports multitenancy and online resource management. Stable operation of 100,000 data tasks per day is supported.
\ No newline at end of file

docs/docs/en/about/hardware.md

@@ -11,7 +11,7 @@ DolphinScheduler, as an open-source distributed workflow task scheduling system,
 | Oracle Enterprise Linux  | 7.0 and above   |
 | Ubuntu LTS               | 16.04 and above |
 
-> **Attention:**
+> **Note:**
 >The above Linux operating systems can run on physical servers and mainstream virtualization environments such as VMware, KVM, and XEN.
 
 ## Recommended Server Configuration
@@ -24,7 +24,7 @@ DolphinScheduler supports 64-bit hardware platforms with Intel x86-64 architectu
 | --- | --- | --- | --- | --- |
 | 4 core+ | 8 GB+ | SAS | GbE | 1+ |
 
-> **Attention:**
+> **Note:**
 > - The above recommended configuration is the minimum configuration for deploying DolphinScheduler. Higher configuration is strongly recommended for production environments.
 > - The recommended hard disk size is more than 50GB and separate the system disk and data disk.
 
@@ -39,7 +39,7 @@ DolphinScheduler provides the following network port configurations for normal o
 | WorkerServer | 1234  | not the communication port, require the native ports do not conflict |
 | ApiApplicationServer |  12345 | backend communication port |
 
-> **Attention:**
+> **Note:**
 > - MasterServer and WorkerServer do not need to enable communication between the networks. As long as the local ports do not conflict.
 > - Administrators can adjust relevant ports on the network side and host-side according to the deployment plan of DolphinScheduler components in the actual environment.
 

docs/docs/en/about/introduction.md

 # About DolphinScheduler
 
-Apache DolphinScheduler is a distributed, easy to extend visual DAG workflow task scheduling open-source system. Solves the intricate dependencies of data R&D ETL and the inability to monitor the health status of tasks. DolphinScheduler assembles tasks in the DAG streaming way, which can monitor the execution status of tasks in time, and supports operations like retry, recovery failure from specified nodes, pause, resume and kill tasks, etc.
+Apache DolphinScheduler provides a distributed and easy to expand visual workflow task scheduling open-source platform. It is suitable for enterprise-level scenarios. It provides a solution to visualize operation tasks, workflows, and the entire data processing procedures.
 
-## Simple to Use
+Apache DolphinScheduler aims to solve complex big data task dependencies and to trigger relationships in data OPS orchestration for various big data applications. Solves the intricate dependencies of data R&D ETL and the inability to monitor the health status of tasks. DolphinScheduler assembles tasks in the Directed Acyclic Graph (DAG) streaming mode, which can monitor the execution status of tasks in time, and supports operations like retry, recovery failure from specified nodes, pause, resume, and kill tasks, etc.
 
-- DolphinScheduler has DAG monitoring user interfaces, users can customize DAG by dragging and dropping. All process definitions are visualized, supports rich third-party systems APIs and one-click deployment.
-
-## High Reliability
-
-- Decentralized multi-masters and multi-workers, support HA, select queues to avoid overload.
-
-## Rich Scenarios
-
-- Support features like multi-tenants, suspend and resume operations to cope with big data scenarios. Support many task types like Spark, Flink, Hive, MR, shell, python, sub_process.
-
-## High Scalability
-
-- Supports customized task types, distributed scheduling, and the overall scheduling capability increases linearly with the scale of the cluster.
\ No newline at end of file
+![Apache DolphinScheduler](../../../img/introduction_ui.png)
\ No newline at end of file

docs/docs/en/guide/homepage.md

 # Home Page
 
-The home page contains task status statistics, process status statistics, and workflow definition statistics for all projects of the user.
+Apache DolphinScheduler home page lets you see task state statistics, workflow state statistics, and project statistics for all projects of users. It is the best way to observe status of your system as a whole as well as diving into individual process to check each status of task and task logs.
 
 ![homepage](../../../img/new_ui/dev/homepage/homepage.png)

docs/docs/en/guide/resource/udf-manage.md

@@ -8,7 +8,7 @@ The resource management and file management functions are similar. The differenc
 
 - Create UDF function
 
-> Click "`Create UDF Function`", enter the UDF function parameters, select the UDF resource, and click `Submit` to create the UDF function.
+> Click `Create UDF Function`, enter the UDF function parameters, select the UDF resource, and click `Submit` to create the UDF function.
 > Currently, only supports temporary UDF functions of `HIVE`.
 
 - UDF function name: Enter the name of the UDF function.

docs/docs/zh/about/features.md

+# 特性
+
+## 简单易用
+
+- **可视化 DAG**: 用户友好的，通过拖拽定义工作流的，运行时控制工具
+- **模块化操作**: 模块化有助于轻松定制和维护。
+
+## 丰富的使用场景
+
+- **支持多种任务类型**: 支持Shell、MR、Spark、SQL等10余种任务类型，支持跨语言，易于扩展
+- **丰富的工作流操作**: 工作流程可以定时、暂停、恢复和停止，便于维护和控制全局和本地参数。
+
+## High Reliability
+
+- **高可靠性**: 去中心化设计，确保稳定性。 原生 HA 任务队列支持，提供过载容错能力。 DolphinScheduler 能提供高度稳健的环境。
+
+## High Scalability
+
+- **高扩展性**: 支持多租户和在线资源管理。支持每天10万个数据任务的稳定运行。

docs/docs/zh/about/introduction.md

 # 关于DolphinScheduler
 
-Apache DolphinScheduler是一个分布式易扩展的可视化DAG工作流任务调度开源系统。解决数据研发ETL 错综复杂的依赖关系，不能直观监控任务健康状态等问题。DolphinScheduler以DAG流式的方式将Task组装起来，可实时监控任务的运行状态，同时支持重试、从指定节点恢复失败、暂停及Kill任务等操作
+Apache DolphinScheduler 是一个分布式易扩展的可视化DAG工作流任务调度开源系统。适用于企业级场景，提供了一个可视化操作任务、工作流和全生命周期数据处理过程的解决方案。
 
-# 简单易用
-DAG监控界面，所有流程定义都是可视化，通过拖拽任务定制DAG，通过API方式与第三方系统对接, 一键部署
-# 高可靠性
-去中心化的多Master和多Worker, 自身支持HA功能, 采用任务队列来避免过载，不会造成机器卡死
-# 丰富的使用场景
-支持暂停恢复操作.支持多租户，更好的应对大数据的使用场景. 支持更多的任务类型，如 spark, hive, mr, python, sub_process, shell
-# 高扩展性
-支持自定义任务类型，调度器使用分布式调度，调度能力随集群线性增长，Master和Worker支持动态上下线
+Apache DolphinScheduler 旨在解决复杂的大数据任务依赖关系，并为应用程序提供数据和各种 OPS 编排中的关系。 解决数据研发ETL依赖错综复杂，无法监控任务健康状态的问题。
+DolphinScheduler 以 DAG（Directed Acyclic Graph，DAG）流式方式组装任务，可以及时监控任务的执行状态，支持重试、指定节点恢复失败、暂停、恢复、终止任务等操作。
+
+![Apache DolphinScheduler](../../../img/introduction_ui.png)
\ No newline at end of file

docs/docs/zh/guide/homepage.md

 # 首页
 
-首页包含用户所有项目的任务状态统计、流程状态统计、工作流定义统计。
+Apache DolphinScheduler 首页可让您查看用户所有项目的任务状态统计、工作流状态统计和项目统计。 这是观察整个系统状态以及深入各个进程以检查任务和任务日志的每个状态的最佳方式。
 
 ![homepage](../../../img/new_ui/dev/homepage/homepage.png)