This is an automated email from the ASF dual-hosted git repository.
dockerzhang pushed a commit to branch master
in repository https://gitbox.apache.org/repos/asf/incubator-inlong-website.git
The following commit(s) were added to refs/heads/master by this push:
new 192901e [INLONG-2437] highlight the massive data scenario for the
brief introduction of InLong (#278)
192901e is described below
commit 192901e63a80ec94349358bcae9f6ba7af4d3a77
Author: dockerzhang <[email protected]>
AuthorDate: Thu Feb 10 16:55:15 2022 +0800
[INLONG-2437] highlight the massive data scenario for the brief
introduction of InLong (#278)
---
.asf.yaml | 2 +-
blog/apache-inlong-0.11.0.md | 6 +++---
blog/apache-inlong-0.12.0.md | 6 +++---
development/how-to-release.md | 2 +-
docs/introduction.md | 4 ++--
docs/modules/manager/overview.md | 2 +-
.../2021-11-26-new-committer-join.md | 4 ++--
i18n/zh-CN/docusaurus-plugin-content-blog/apache-inlong-0.11.0.md | 4 ++--
i18n/zh-CN/docusaurus-plugin-content-blog/apache-inlong-0.12.0.md | 4 ++--
.../current/how-to-release.md | 2 +-
i18n/zh-CN/docusaurus-plugin-content-docs/current/introduction.md | 4 ++--
.../current/modules/manager/overview.md | 2 +-
.../version-0.11.0/modules/manager/architecture.md | 2 +-
.../version-0.11.0/user_guide/quick_start.md | 2 +-
.../docusaurus-plugin-content-docs/version-0.12.0/introduction.md | 4 ++--
.../version-0.12.0/modules/manager/overview.md | 2 +-
news/2021-11-26-new-committer-join.md | 4 ++--
src/pages/Home/config.json | 4 ++--
versioned_docs/version-0.11.0/modules/manager/architecture.md | 2 +-
versioned_docs/version-0.11.0/user_guide/quick_start.md | 2 +-
versioned_docs/version-0.12.0/introduction.md | 4 ++--
versioned_docs/version-0.12.0/modules/manager/overview.md | 2 +-
22 files changed, 35 insertions(+), 35 deletions(-)
diff --git a/.asf.yaml b/.asf.yaml
index 373ce39..06eeaf8 100644
--- a/.asf.yaml
+++ b/.asf.yaml
@@ -1,5 +1,5 @@
github:
- description: "Apache InLong - a one-stop data streaming platform"
+ description: "Apache InLong - a one-stop integration framework for massive
data"
homepage: https://inlong.apache.org/
features:
# Enable issues management
diff --git a/blog/apache-inlong-0.11.0.md b/blog/apache-inlong-0.11.0.md
index bdc4c15..982a124 100644
--- a/blog/apache-inlong-0.11.0.md
+++ b/blog/apache-inlong-0.11.0.md
@@ -3,7 +3,7 @@ title: Apache InLong 0.11.0
sidebar_position: 2
---
-Apache InLong (incubating) has been renamed from the original Apache TubeMQ
(incubating) from 0.9.0. With the name change, InLong has also been upgraded
from a single message queue to a one-stop data integration solution. InLong
supports data collection, aggregation, caching, and sorting, users can
import data from the data source to the real-time computing engine or land to
offline storage with a simple configuration.
+Apache InLong (incubating) has been renamed from the original Apache TubeMQ
(incubating) from 0.9.0. With the name change, InLong has also been upgraded
from a single message queue to a one-stop integration framework for massive
data. InLong supports data collection, aggregation, caching, and sorting,
users can import data from the data source to the real-time computing engine or
land to offline storage with a simple configuration.
The just-released version `0.11.0-incubating` is the third version after the
name changed. This version includes next features:
- Lower the user's threshold for use further. Support all modules of InLong
to be deployed on Kubernetes, and refactor the official website, so that
users can more easily access related documents.
- Support more usage scenarios, increase the data flow direction of
`Dataproxy -> Pulsar`, and cover scenarios with higher requirements for data
integrity and consistency.
@@ -12,8 +12,8 @@ The just-released version `0.11.0-incubating` is the third
version after the nam
This version closed more than 80 issues, including four significant features
and 35 improvements.
### Apache InLong(incubating) Introduction
-[Apache InLong](https://inlong.apache.org) is a one-stop data access platform
donated by Tencent to the Apache community. It provides automatic, safe,
reliable, and high-performance data transmission capabilities to facilitate
the construction of streaming-based data analysis, modeling, and
applications.
-The Apache InLong project was originally called TubeMQ, focusing on
high-performance, low-cost message queuing services. In order to further
release the surrounding ecological capabilities of TubeMQ, we upgraded the
project to InLong, focusing on creating a one-stop data integration solution.
+[Apache InLong](https://inlong.apache.org) is a one-stop integration framework
for massive data donated by Tencent to the Apache community. It provides
automatic, safe, reliable, and high-performance data transmission
capabilities to facilitate the construction of streaming-based data analysis,
modeling, and applications.
+The Apache InLong project was originally called TubeMQ, focusing on
high-performance, low-cost message queuing services. In order to further
release the surrounding ecological capabilities of TubeMQ, we upgraded the
project to InLong, focusing on creating a one-stop integration framework for
massive data.
Apache InLong uses TDBank internally used by Tencent as the prototype, and
relies on trillion-level data access and processing capabilities to integrate
the entire process of data collection, aggregation, storage, and sorting
data processing. It is simple to use, flexible to expand, stable and
reliable characteristic.
<img src="/img/inlong-structure-en.png" align="center" alt="Apache InLong"/>
diff --git a/blog/apache-inlong-0.12.0.md b/blog/apache-inlong-0.12.0.md
index ab14e53..e28e757 100644
--- a/blog/apache-inlong-0.12.0.md
+++ b/blog/apache-inlong-0.12.0.md
@@ -5,7 +5,7 @@ sidebar_position: 1
InLong: the sacred animal in Chinese myths stories, draws rivers into the sea,
as a metaphor for the InLong system to provide data access capabilities.
-Apache InLong is a one-stop data integration framework that provides
automatic, secure and reliable data transmission capabilities. InLong supports
both batch and stream data processing at the same time, which offers great
power to build data analysis, modeling and other real-time applications based
on streaming data.
+Apache InLong is a one-stop integration framework for massive data that
provides automatic, secure and reliable data transmission capabilities. InLong
supports both batch and stream data processing at the same time, which offers
great power to build data analysis, modeling and other real-time applications
based on streaming data.
The 0.12.0-incubating just released mainly includes the following:
- Provide automatic, safe, reliable and high-performance data transmission
capabilities, while supporting batch and streaming
- Refactor the document structure of the official website to facilitate users
to consult related documents based on the main line
@@ -17,8 +17,8 @@ This version closed about 120+ issues, including four major
features and 35 impr
### Apache InLong(incubating) Introduction
-[Apache InLong](https://inlong.apache.org) is a one-stop data integration
framework donated by Tencent to the Apache community. It provides automatic,
safe, reliable, and high-performance data transmission capabilities to
facilitate the construction of streaming-based data analysis, modeling, and
applications.
-The Apache InLong project was originally called TubeMQ, focusing on
high-performance, low-cost message queuing services. In order to further
release the surrounding ecological capabilities of TubeMQ, we upgraded the
project to InLong, focusing on creating a one-stop data integration solution.
+[Apache InLong](https://inlong.apache.org) is a one-stop integration framework
for massive data donated by Tencent to the Apache community. It provides
automatic, safe, reliable, and high-performance data transmission
capabilities to facilitate the construction of streaming-based data analysis,
modeling, and applications.
+The Apache InLong project was originally called TubeMQ, focusing on
high-performance, low-cost message queuing services. In order to further
release the surrounding ecological capabilities of TubeMQ, we upgraded the
project to InLong, focusing on creating a one-stop integration framework for
massive data.
Apache InLong uses TDBank internally used by Tencent as the prototype, and
relies on trillion-level data access and processing capabilities to integrate
the entire process of data collection, aggregation, storage, and sorting
data processing. It is simple to use, flexible to expand, stable and
reliable characteristic.
<img src="/img/inlong-structure-en.png" align="center" alt="Apache InLong"/>
diff --git a/development/how-to-release.md b/development/how-to-release.md
index 136f75e..19e9f4c 100644
--- a/development/how-to-release.md
+++ b/development/how-to-release.md
@@ -486,7 +486,7 @@ Hi all,
The Apache InLong(incubating) community is pleased to announce
that Apache InLong(incubating) ${release_version} has been released!
-Apache InLong is a one-stop data streaming platform that provides automatic,
secure,
+Apache InLong is a one-stop integration framework for massive data that
provides automatic, secure,
distributed, and efficient data publishing and subscription capabilities.
This platform helps you easily build stream-based data applications.
diff --git a/docs/introduction.md b/docs/introduction.md
index 69f11f1..0b55520 100644
--- a/docs/introduction.md
+++ b/docs/introduction.md
@@ -7,8 +7,8 @@ sidebar_position: 1
> it is regarded as a metaphor of the InLong system for reporting streams of
> data.
## About InLong
-[Apache InLong](https://inlong.apache.org) is a one-stop data integration
framework donated by Tencent to the Apache community. It provides automatic,
safe, reliable, and high-performance data transmission capabilities to
facilitate the construction of streaming-based data analysis, modeling, and
applications.
-The Apache InLong project was originally called TubeMQ, focusing on
high-performance, low-cost message queuing services. In order to further
release the surrounding ecological capabilities of TubeMQ, we upgraded the
project to InLong, focusing on creating a one-stop data integration solution.
+[Apache InLong](https://inlong.apache.org) is a one-stop integration framework
for massive data donated by Tencent to the Apache community. It provides
automatic, safe, reliable, and high-performance data transmission
capabilities to facilitate the construction of streaming-based data analysis,
modeling, and applications.
+The Apache InLong project was originally called TubeMQ, focusing on
high-performance, low-cost message queuing services. In order to further
release the surrounding ecological capabilities of TubeMQ, we upgraded the
project to InLong, focusing on creating a one-stop integration framework for
massive data.
Apache InLong uses TDBank internally used by Tencent as the prototype, and
relies on trillion-level data access and processing capabilities to integrate
the entire process of data collection, aggregation, storage, and sorting
data processing. It is simple to use, flexible to expand, stable and
reliable characteristic.
## Features
diff --git a/docs/modules/manager/overview.md b/docs/modules/manager/overview.md
index 27ee1f4..c6c243e 100644
--- a/docs/modules/manager/overview.md
+++ b/docs/modules/manager/overview.md
@@ -4,7 +4,7 @@ title: Overview
## 1 Introduction to Apache InLong Manager
-+ Target positioning: Apache inlong is positioned as a one-stop data
integration framework, providing complete coverage of big data access scenarios
from data collection, transmission, sorting, and technical capabilities.
++ Target positioning: Apache inlong is positioned as a one-stop integration
framework for massive data, providing complete coverage of big data access
scenarios from data collection, transmission, sorting, and technical
capabilities.
+ Platform value: Users can complete task configuration, management, and
indicator monitoring through the platform's built-in management and
configuration platform. At the same time, the platform provides SPI extension
points in the main links of the process to implement custom logic as needed.
Ensure stable and efficient functions while lowering the threshold for platform
use.
diff --git
a/i18n/zh-CN/docusaurus-plugin-content-blog-news/2021-11-26-new-committer-join.md
b/i18n/zh-CN/docusaurus-plugin-content-blog-news/2021-11-26-new-committer-join.md
index f827a74..aff1b9b 100644
---
a/i18n/zh-CN/docusaurus-plugin-content-blog-news/2021-11-26-new-committer-join.md
+++
b/i18n/zh-CN/docusaurus-plugin-content-blog-news/2021-11-26-new-committer-join.md
@@ -6,9 +6,9 @@ InLong(应龙) : 中国神话故事里的神兽，引流入海，借喻 InLong
### Apache InLong(incubating) 简介
-[Apache InLong（应龙）](https://inlong.apache.org)是腾讯捐献给 Apache
社区的一站式的数据接入平台，提供自动、安全、可靠和高性能的数据传输能力，方便业务构建基于流式的数据分析、建模和应用。
+[Apache InLong（应龙）](https://inlong.apache.org)是腾讯捐献给 Apache
社区的一站式海量数据集成框架，提供自动、安全、可靠和高性能的数据传输能力，方便业务构建基于流式的数据分析、建模和应用。
-InLong 项目原名 TubeMQ ，专注于高性能、低成本的消息队列服务。为了进一步释放 TubeMQ 周边的生态能力，我们将项目升级为
InLong，专注打造一站式数据流接入服务平台。
+InLong 项目原名 TubeMQ ，专注于高性能、低成本的消息队列服务。为了进一步释放 TubeMQ 周边的生态能力，我们将项目升级为
InLong，专注打造一站式海量数据集成框架。
Apache InLong 以腾讯内部使用的 TDBank
为原型，依托万亿级别的数据接入和处理能力，整合了数据采集、汇聚、存储、分拣数据处理全流程，拥有简单易用、灵活扩展、稳定可靠等特性。
<img src="/img/inlong-structure-zh.png" align="center" alt="Apache InLong"/>
diff --git a/i18n/zh-CN/docusaurus-plugin-content-blog/apache-inlong-0.11.0.md
b/i18n/zh-CN/docusaurus-plugin-content-blog/apache-inlong-0.11.0.md
index ef23218..7c750f1 100644
--- a/i18n/zh-CN/docusaurus-plugin-content-blog/apache-inlong-0.11.0.md
+++ b/i18n/zh-CN/docusaurus-plugin-content-blog/apache-inlong-0.11.0.md
@@ -3,7 +3,7 @@ title: 0.11.0 版本发布
sidebar_position: 2
---
-Apache InLong(incubating) 从 0.9.0 版本开始由原 Apache
TubeMQ（incubating）改名而来，伴随着名称的改变，InLong
也由单一的消息队列升级为一站式的数据集成解决方案，支持了大数据领域的采集、汇聚、缓存和分拣功能，用户只需要简单的配置就可以把数据从数据源导入到实时计算引擎或者落地到离线存储。
+Apache InLong(incubating) 从 0.9.0 版本开始由原 Apache
TubeMQ（incubating）改名而来，伴随着名称的改变，InLong
也由单一的消息队列升级为一站式海量数据集成框架，支持了大数据领域的采集、汇聚、缓存和分拣功能，用户只需要简单的配置就可以把数据从数据源导入到实时计算引擎或者落地到离线存储。
刚刚发布的 0.11.0-incubating 版本是改名之后的第三个版本，这个版本：
- 进一步降低用户的使用门槛，支持 InLong 所有模块 on Kubernets，并且对官网进行了重构，用户可以更加方便的查阅相关文档
- 支持更多的业务场景，增加 Dataproxy -> Pulsar 的数据流向，覆盖对数据完整性、一致性要求更高的场景
@@ -12,7 +12,7 @@ Apache InLong(incubating) 从 0.9.0 版本开始由原 Apache
TubeMQ（incubatin
该版本关闭超过 80 个 issue, 包含了四个重大 feature 和 35 个 improvements 。
### Apache InLong(incubating) 简介
-[Apache InLong（应龙）](https://inlong.apache.org/zh-cn/)是腾讯捐献给 Apache
社区的一站式数据接入平台，提供自动、安全、可靠和高性能的数据传输能力，方便业务构建基于流式的数据分析、建模和应用。InLong 项目原本叫TubeMQ
，专注高性能、低成本的消息队列服务。为了进一步释放 TubeMQ 周边生态能力，我们将项目升级为 InLong ，专注打造一站式的数据集成解决方案。
+[Apache InLong（应龙）](https://inlong.apache.org/zh-cn/)是腾讯捐献给 Apache
社区的一站式海量数据集成框架，提供自动、安全、可靠和高性能的数据传输能力，方便业务构建基于流式的数据分析、建模和应用。InLong 项目原本叫TubeMQ
，专注高性能、低成本的消息队列服务。为了进一步释放 TubeMQ 周边生态能力，我们将项目升级为 InLong ，专注打造一站式的数据集成解决方案。
Apache InLong 以腾讯内部使用的 TDBank
为原型，依托万亿级别的数据接入和处理能力，整合了数据采集、汇聚、存储、分拣数据处理全流程，拥有简单易用、灵活扩展、稳定可靠等特性。
<img src="/img/inlong_architecture.png" align="center" alt="Apache InLong"/>
diff --git a/i18n/zh-CN/docusaurus-plugin-content-blog/apache-inlong-0.12.0.md
b/i18n/zh-CN/docusaurus-plugin-content-blog/apache-inlong-0.12.0.md
index 9edc548..97da3fa 100644
--- a/i18n/zh-CN/docusaurus-plugin-content-blog/apache-inlong-0.12.0.md
+++ b/i18n/zh-CN/docusaurus-plugin-content-blog/apache-inlong-0.12.0.md
@@ -5,7 +5,7 @@ sidebar_position: 1
InLong(应龙) : 中国神话故事里的神兽，引流入海，借喻 InLong 系统提供数据接入能力。
-Apache
InLong（应龙）是一个一站式的数据集成框架，提供自动、安全、可靠和高性能的数据传输能力，同时支持批和流，方便业务构建基于流式的数据分析、建模和应用。InLong支持大数据领域的采集、汇聚、缓存和分拣功能，用户只需要简单的配置就可以把数据从数据源导入到实时计算引擎或者落地到离线存储。
+Apache
InLong（应龙）是一个一站式海量数据集成框架，提供自动、安全、可靠和高性能的数据传输能力，同时支持批和流，方便业务构建基于流式的数据分析、建模和应用。InLong支持大数据领域的采集、汇聚、缓存和分拣功能，用户只需要简单的配置就可以把数据从数据源导入到实时计算引擎或者落地到离线存储。
刚刚发布的 0.12.0-incubating 主要包括以下内容：
- 提供自动、安全、可靠和高性能的数据传输能力，同时支持批和流
@@ -17,7 +17,7 @@ Apache InLong（应龙）是一个一站式的数据集成框架，提供自动
该版本关闭了约 120+ 个 issue，包含四个重大 feature 和 35 个 improvements。
### Apache InLong(incubating) 简介
-[Apache InLong（应龙）](https://inlong.apache.org/zh-cn/) 是腾讯捐献给 Apache
社区的一站式的数据集成框架，提供自动、安全、可靠和高性能的数据传输能力，方便业务构建基于流式的数据分析、建模和应用。 InLong 项目原名 TubeMQ
，专注于高性能、低成本的消息队列服务。为了进一步释放 TubeMQ 周边的生态能力，我们将项目升级为 InLong，专注打造一站式数据流接入服务平台。
+[Apache InLong（应龙）](https://inlong.apache.org/zh-cn/) 是腾讯捐献给 Apache
社区的一站式海量数据集成框架，提供自动、安全、可靠和高性能的数据传输能力，方便业务构建基于流式的数据分析、建模和应用。 InLong 项目原名 TubeMQ
，专注于高性能、低成本的消息队列服务。为了进一步释放 TubeMQ 周边的生态能力，我们将项目升级为 InLong，专注打造一站式数据流接入服务平台。
Apache InLong 以腾讯内部使用的 TDBank
为原型，具有万亿级数据的接入和处理能力，整合了数据采集、汇聚、存储、分拣数据处理全流程，拥有简单易用、灵活扩展、稳定可靠等特性。
<img src="/img/inlong_architecture.png" align="center" alt="Apache InLong"/>
diff --git
a/i18n/zh-CN/docusaurus-plugin-content-docs-development/current/how-to-release.md
b/i18n/zh-CN/docusaurus-plugin-content-docs-development/current/how-to-release.md
index 4075a69..9508b45 100644
---
a/i18n/zh-CN/docusaurus-plugin-content-docs-development/current/how-to-release.md
+++
b/i18n/zh-CN/docusaurus-plugin-content-docs-development/current/how-to-release.md
@@ -499,7 +499,7 @@ Hi all,
The Apache InLong(incubating) community is pleased to announce
that Apache InLong(incubating) ${release_version} has been released!
-Apache InLong is a one-stop data streaming platform that provides automatic,
secure,
+Apache InLong is a one-stop integration framework for massive data that
provides automatic, secure,
distributed, and efficient data publishing and subscription capabilities.
This platform helps you easily build stream-based data applications.
diff --git a/i18n/zh-CN/docusaurus-plugin-content-docs/current/introduction.md
b/i18n/zh-CN/docusaurus-plugin-content-docs/current/introduction.md
index 01eef19..dc94122 100644
--- a/i18n/zh-CN/docusaurus-plugin-content-docs/current/introduction.md
+++ b/i18n/zh-CN/docusaurus-plugin-content-docs/current/introduction.md
@@ -6,8 +6,8 @@ sidebar_position: 1
> InLong(应龙)，中国神话故事里的神兽，引流入海，借喻 InLong 系统提供数据接入能力。
## 关于 InLong
-[Apache InLong（应龙）](https://inlong.apache.org)是腾讯捐献给 Apache
社区的一站式的数据集成框架，提供自动、安全、可靠和高性能的数据传输能力，方便业务构建基于流式的数据分析、建模和应用。
-InLong 项目原名 TubeMQ ，专注于高性能、低成本的消息队列服务。为了进一步释放 TubeMQ 周边的生态能力，我们将项目升级为
InLong，专注打造一站式数据流接入服务平台。
+[Apache InLong（应龙）](https://inlong.apache.org)是腾讯捐献给 Apache
社区的一站式海量数据集成框架，提供自动、安全、可靠和高性能的数据传输能力，方便业务构建基于流式的数据分析、建模和应用。
+InLong 项目原名 TubeMQ ，专注于高性能、低成本的消息队列服务。为了进一步释放 TubeMQ 周边的生态能力，我们将项目升级为
InLong，专注打造一站式海量数据集成框架。
Apache InLong 以腾讯内部使用的 TDBank
为原型，依托万亿级别的数据接入和处理能力，整合了数据采集、汇聚、存储、分拣数据处理全流程，拥有简单易用、灵活扩展、稳定可靠等特性。
## 特性
diff --git
a/i18n/zh-CN/docusaurus-plugin-content-docs/current/modules/manager/overview.md
b/i18n/zh-CN/docusaurus-plugin-content-docs/current/modules/manager/overview.md
index bc3e9ed..cfb43a2 100644
---
a/i18n/zh-CN/docusaurus-plugin-content-docs/current/modules/manager/overview.md
+++
b/i18n/zh-CN/docusaurus-plugin-content-docs/current/modules/manager/overview.md
@@ -4,7 +4,7 @@ title: 总览
## 1 Apache InLong Manager介绍
-+ 目标定位：Apache inlong 定位为一站式数据接入解决方案，提供完整覆盖大数据接入场景从数据采集、传输、分拣、落地的技术能力。
++ 目标定位：Apache inlong 定位为一站式海量数据集成框架，提供完整覆盖大数据接入场景从数据采集、传输、分拣、落地的技术能力。
+
平台价值：用户可以通过平台中自带的管理、配置平台完成任务的配置、管理、指标监控，同时通过平台在流程中各主要环节提供SPI扩展点按需要实现自定义逻辑。保证功能稳定高效的同时降低平台使用门槛。
diff --git
a/i18n/zh-CN/docusaurus-plugin-content-docs/version-0.11.0/modules/manager/architecture.md
b/i18n/zh-CN/docusaurus-plugin-content-docs/version-0.11.0/modules/manager/architecture.md
index 9a01a35..684700e 100644
---
a/i18n/zh-CN/docusaurus-plugin-content-docs/version-0.11.0/modules/manager/architecture.md
+++
b/i18n/zh-CN/docusaurus-plugin-content-docs/version-0.11.0/modules/manager/architecture.md
@@ -4,7 +4,7 @@ title: 架构介绍
## Apache InLong Manager介绍
-+ 目标定位：Apache inlong 定位为一站式数据接入解决方案，提供完整覆盖大数据接入场景从数据采集、传输、分拣、落地的技术能力。
++ 目标定位：Apache inlong 定位为一站式海量数据集成框架，提供完整覆盖大数据接入场景从数据采集、传输、分拣、落地的技术能力。
+
平台价值：用户可以通过平台中自带的管理、配置平台完成任务的配置、管理、指标监控，同时通过平台在流程中各主要环节提供SPI扩展点按需要实现自定义逻辑。保证功能稳定高效的同时降低平台使用门槛。
diff --git
a/i18n/zh-CN/docusaurus-plugin-content-docs/version-0.11.0/user_guide/quick_start.md
b/i18n/zh-CN/docusaurus-plugin-content-docs/version-0.11.0/user_guide/quick_start.md
index e0958a5..446b42c 100644
---
a/i18n/zh-CN/docusaurus-plugin-content-docs/version-0.11.0/user_guide/quick_start.md
+++
b/i18n/zh-CN/docusaurus-plugin-content-docs/version-0.11.0/user_guide/quick_start.md
@@ -8,7 +8,7 @@ sidebar_position: 1
## 整体架构
<img src="/img/inlong_architecture.png" align="center" alt="Apache InLong"/>
-[Apache InLong](https://inlong.apache.org)(incubating)
整体架构如上，该组件是一站式数据流媒体平台，提供自动化、安全、分布式、高效的数据发布和订阅能力，帮助您轻松构建基于流的数据应用程序。
+[Apache InLong](https://inlong.apache.org)(incubating)
整体架构如上，为一站式海量数据集成框架，提供自动化、安全、分布式、高效的数据发布和订阅能力，帮助您轻松构建基于流的数据应用程序。
InLong(应龙)是中国神话故事里的神兽，可以引流入海，借喻InLong可用于流式数据上报功能。
diff --git
a/i18n/zh-CN/docusaurus-plugin-content-docs/version-0.12.0/introduction.md
b/i18n/zh-CN/docusaurus-plugin-content-docs/version-0.12.0/introduction.md
index 80c1d9b..3612033 100644
--- a/i18n/zh-CN/docusaurus-plugin-content-docs/version-0.12.0/introduction.md
+++ b/i18n/zh-CN/docusaurus-plugin-content-docs/version-0.12.0/introduction.md
@@ -6,8 +6,8 @@ sidebar_position: 1
> InLong(应龙)，中国神话故事里的神兽，引流入海，借喻 InLong 系统提供数据接入能力。
## 关于 InLong
-[Apache InLong（应龙）](https://inlong.apache.org)是腾讯捐献给 Apache
社区的一站式的数据集成框架，提供自动、安全、可靠和高性能的数据传输能力，方便业务构建基于流式的数据分析、建模和应用。
-InLong 项目原名 TubeMQ ，专注于高性能、低成本的消息队列服务。为了进一步释放 TubeMQ 周边的生态能力，我们将项目升级为
InLong，专注打造一站式数据流接入服务平台。
+[Apache InLong（应龙）](https://inlong.apache.org)是腾讯捐献给 Apache
社区的一站式海量数据集成框架，提供自动、安全、可靠和高性能的数据传输能力，方便业务构建基于流式的数据分析、建模和应用。
+InLong 项目原名 TubeMQ ，专注于高性能、低成本的消息队列服务。为了进一步释放 TubeMQ 周边的生态能力，我们将项目升级为
InLong，专注打造一站式海量数据集成框架。
Apache InLong 以腾讯内部使用的 TDBank
为原型，依托万亿级别的数据接入和处理能力，整合了数据采集、汇聚、存储、分拣数据处理全流程，拥有简单易用、灵活扩展、稳定可靠等特性。
## 特性
diff --git
a/i18n/zh-CN/docusaurus-plugin-content-docs/version-0.12.0/modules/manager/overview.md
b/i18n/zh-CN/docusaurus-plugin-content-docs/version-0.12.0/modules/manager/overview.md
index bc3e9ed..cfb43a2 100644
---
a/i18n/zh-CN/docusaurus-plugin-content-docs/version-0.12.0/modules/manager/overview.md
+++
b/i18n/zh-CN/docusaurus-plugin-content-docs/version-0.12.0/modules/manager/overview.md
@@ -4,7 +4,7 @@ title: 总览
## 1 Apache InLong Manager介绍
-+ 目标定位：Apache inlong 定位为一站式数据接入解决方案，提供完整覆盖大数据接入场景从数据采集、传输、分拣、落地的技术能力。
++ 目标定位：Apache inlong 定位为一站式海量数据集成框架，提供完整覆盖大数据接入场景从数据采集、传输、分拣、落地的技术能力。
+
平台价值：用户可以通过平台中自带的管理、配置平台完成任务的配置、管理、指标监控，同时通过平台在流程中各主要环节提供SPI扩展点按需要实现自定义逻辑。保证功能稳定高效的同时降低平台使用门槛。
diff --git a/news/2021-11-26-new-committer-join.md
b/news/2021-11-26-new-committer-join.md
index 5f7c4a0..2a6a37d 100644
--- a/news/2021-11-26-new-committer-join.md
+++ b/news/2021-11-26-new-committer-join.md
@@ -6,8 +6,8 @@ InLong (应龙) is a divine beast in Chinese mythology who guides
river into the
### Apache InLong(incubating) Introduction
-[Apache InLong](https://inlong.apache.org) is a one-stop data access platform
donated by Tencent to the Apache community. It provides automatic, safe,
reliable, and high-performance data transmission capabilities to facilitate
the construction of streaming-based data analysis, modeling, and
applications.
-The Apache InLong project was originally called TubeMQ, focusing on
high-performance, low-cost message queuing services. In order to further
release the surrounding ecological capabilities of TubeMQ, we upgraded the
project to InLong, focusing on creating a one-stop data integration solution.
+[Apache InLong](https://inlong.apache.org) is a one-stop integration framework
for massive data donated by Tencent to the Apache community. It provides
automatic, safe, reliable, and high-performance data transmission
capabilities to facilitate the construction of streaming-based data analysis,
modeling, and applications.
+The Apache InLong project was originally called TubeMQ, focusing on
high-performance, low-cost message queuing services. In order to further
release the surrounding ecological capabilities of TubeMQ, we upgraded the
project to InLong, focusing on creating a one-stop integration framework for
massive data.
Apache InLong uses TDBank internally used by Tencent as the prototype, and
relies on trillion-level data access and processing capabilities to integrate
the entire process of data collection, aggregation, storage, and sorting
data processing. It is simple to use, flexible to expand, stable and
reliable characteristic.
<img src="/img/inlong-structure-en.png" align="center" alt="Apache InLong"/>
diff --git a/src/pages/Home/config.json b/src/pages/Home/config.json
index 403fde8..8d8f792 100644
--- a/src/pages/Home/config.json
+++ b/src/pages/Home/config.json
@@ -3,7 +3,7 @@
"brand": {
"brandName": "Apache",
"projectName": "InLong",
- "briefIntroduction": "Apache
InLong（应龙）是一个一站式的数据集成框架，提供自动、安全、可靠和高性能的数据传输能力，同时支持批和流，方便业务构建基于流式的数据分析、建模和应用。",
+ "briefIntroduction": "Apache
InLong（应龙）是一个一站式海量数据集成框架，提供自动、安全、可靠和高性能的数据传输能力，同时支持批和流，方便业务构建基于流式的数据分析、建模和应用。",
"features": [
"自动",
"安全",
@@ -67,7 +67,7 @@
"High performance",
"Distributed"
],
- "briefIntroduction": "Apache InLong is a one-stop data integration
framework that provides automatic, secure and reliable data transmission
capabilities. InLong supports both batch and stream data processing at the same
time, which offers great power to build data analysis, modeling and other
real-time applications based on streaming data.",
+ "briefIntroduction": "Apache InLong is a one-stop integration
framework for massive data that provides automatic, secure and reliable data
transmission capabilities. InLong supports both batch and stream data
processing at the same time, which offers great power to build data analysis,
modeling and other real-time applications based on streaming data.",
"buttons": [
{
"text": "Quick Start",
diff --git a/versioned_docs/version-0.11.0/modules/manager/architecture.md
b/versioned_docs/version-0.11.0/modules/manager/architecture.md
index 84256a9..61e2fcb 100644
--- a/versioned_docs/version-0.11.0/modules/manager/architecture.md
+++ b/versioned_docs/version-0.11.0/modules/manager/architecture.md
@@ -4,7 +4,7 @@ title: Architecture
## Introduction to Apache InLong Manager
-+ Target positioning: Apache inlong is positioned as a one-stop data access
solution, providing complete coverage of big data access scenarios from data
collection, transmission, sorting, and technical capabilities.
++ Target positioning: Apache inlong is positioned as a one-stop integration
framework for massive data, providing complete coverage of big data access
scenarios from data collection, transmission, sorting, and technical
capabilities.
+ Platform value: Users can complete task configuration, management, and
indicator monitoring through the platform's built-in management and
configuration platform. At the same time, the platform provides SPI extension
points in the main links of the process to implement custom logic as needed.
Ensure stable and efficient functions while lowering the threshold for platform
use.
diff --git a/versioned_docs/version-0.11.0/user_guide/quick_start.md
b/versioned_docs/version-0.11.0/user_guide/quick_start.md
index 218dd2b..f7eb15d 100644
--- a/versioned_docs/version-0.11.0/user_guide/quick_start.md
+++ b/versioned_docs/version-0.11.0/user_guide/quick_start.md
@@ -8,7 +8,7 @@ This section contains a quick start guide to help you get
started with Apache In
## Overall architecture
<img src="/img/inlong_architecture.png" align="center" alt="Apache InLong"/>
-[Apache InLong](https://inlong.apache.org)(incubating) overall architecture is
as above. This component is a one-stop data streaming platform that provides
automated, secure, distributed, and efficient data publishing and subscription
capabilities to help You can easily build stream-based data applications.
+[Apache InLong](https://inlong.apache.org)(incubating) overall architecture is
as above. This component is a one-stop integration framework for massive data
that provides automated, secure, distributed, and efficient data publishing and
subscription capabilities to help You can easily build stream-based data
applications.
InLong (应龙) is a divine beast in Chinese mythology who guides river into the
sea, it is regarded as a metaphor of the InLong system for reporting streams of
data.
diff --git a/versioned_docs/version-0.12.0/introduction.md
b/versioned_docs/version-0.12.0/introduction.md
index 47c4b95..dd77e56 100644
--- a/versioned_docs/version-0.12.0/introduction.md
+++ b/versioned_docs/version-0.12.0/introduction.md
@@ -7,8 +7,8 @@ sidebar_position: 1
> it is regarded as a metaphor of the InLong system for reporting streams of
> data.
## About InLong
-[Apache InLong](https://inlong.apache.org) is a one-stop data ingestion
platform donated by Tencent to the Apache community. It provides automatic,
safe, reliable, and high-performance data transmission capabilities to
facilitate the construction of streaming-based data analysis, modeling, and
applications.
-The Apache InLong project was originally called TubeMQ, focusing on
high-performance, low-cost message queuing services. In order to further
release the surrounding ecological capabilities of TubeMQ, we upgraded the
project to InLong, focusing on creating a one-stop data integration solution.
+[Apache InLong](https://inlong.apache.org) is a one-stop integration framework
for massive data donated by Tencent to the Apache community. It provides
automatic, safe, reliable, and high-performance data transmission
capabilities to facilitate the construction of streaming-based data analysis,
modeling, and applications.
+The Apache InLong project was originally called TubeMQ, focusing on
high-performance, low-cost message queuing services. In order to further
release the surrounding ecological capabilities of TubeMQ, we upgraded the
project to InLong, focusing on creating a one-stop integration framework for
massive data.
Apache InLong uses TDBank internally used by Tencent as the prototype, and
relies on trillion-level data access and processing capabilities to integrate
the entire process of data collection, aggregation, storage, and sorting
data processing. It is simple to use, flexible to expand, stable and
reliable characteristic.
## Features
diff --git a/versioned_docs/version-0.12.0/modules/manager/overview.md
b/versioned_docs/version-0.12.0/modules/manager/overview.md
index 27ee1f4..c6c243e 100644
--- a/versioned_docs/version-0.12.0/modules/manager/overview.md
+++ b/versioned_docs/version-0.12.0/modules/manager/overview.md
@@ -4,7 +4,7 @@ title: Overview
## 1 Introduction to Apache InLong Manager
-+ Target positioning: Apache inlong is positioned as a one-stop data
integration framework, providing complete coverage of big data access scenarios
from data collection, transmission, sorting, and technical capabilities.
++ Target positioning: Apache inlong is positioned as a one-stop integration
framework for massive data, providing complete coverage of big data access
scenarios from data collection, transmission, sorting, and technical
capabilities.
+ Platform value: Users can complete task configuration, management, and
indicator monitoring through the platform's built-in management and
configuration platform. At the same time, the platform provides SPI extension
points in the main links of the process to implement custom logic as needed.
Ensure stable and efficient functions while lowering the threshold for platform
use.
