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     new 72f642da25 [INLONG-8419][Doc] Update the description about InLong 
(#8420)
72f642da25 is described below

commit 72f642da2572532730e53fae6a92ba3b0778c9cb
Author: Charles Zhang <[email protected]>
AuthorDate: Tue Jul 4 19:29:50 2023 +0800

    [INLONG-8419][Doc] Update the description about InLong (#8420)
---
 .asf.yaml | 5 +++--
 README.md | 6 +++---
 pom.xml   | 2 +-
 3 files changed, 7 insertions(+), 6 deletions(-)

diff --git a/.asf.yaml b/.asf.yaml
index 6fc2b2a158..38273d0463 100644
--- a/.asf.yaml
+++ b/.asf.yaml
@@ -19,13 +19,14 @@
 # of the project and make sure to discuss the changes with dev@ before 
committing.
 
 github:
-  description: "Apache InLong - a one-stop integration framework for massive 
data"
+  description: "Apache InLong - a one-stop, full-scenario integration 
framework for massive data"
   homepage: https://inlong.apache.org/
   labels:
     - inlong
+    - framework
     - one-stop-service
+    - full-scenario-service
     - massive-data-integration
-    - framework
     - data-streaming
     - event-streaming
   features:
diff --git a/README.md b/README.md
index ced3449496..6c09eca8ba 100644
--- a/README.md
+++ b/README.md
@@ -20,7 +20,7 @@
 -->
 
 
-# [A one-stop integration framework for massive 
data](https://inlong.apache.org/)
+# [A one-stop, full-scenario integration framework for massive 
data](https://inlong.apache.org/)
 [![GitHub 
Actions](https://github.com/apache/inlong/actions/workflows/ci_build.yml/badge.svg)](https://github.com/apache/inlong/actions)
 
[![CodeCov](https://codecov.io/gh/apache/inlong/branch/master/graph/badge.svg)](https://codecov.io/gh/apache/inlong)
 [![Maven 
Central](https://maven-badges.herokuapp.com/maven-central/org.apache.inlong/inlong/badge.svg)](http://search.maven.org/#search%7Cga%7C1%7Corg.apache.inlong)
@@ -44,7 +44,7 @@
 
|:-----------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
 | [![Stargazers over 
time](https://starchart.cc/apache/inlong.svg)](https://starchart.cc/apache/inlong)
 | [![Contributor Over 
Time](https://contributor-overtime-api.git-contributor.com/contributors-svg?chart=contributorOverTime&repo=apache/inlong)](https://git-contributor.com?chart=contributorOverTime&repo=apache/inlong)
 |
 
-[Apache InLong](https://inlong.apache.org) 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.
+[Apache InLong](https://inlong.apache.org) is a one-stop, full-scenario 
integration framework for massive data that supports `Data Ingestion`, `Data 
Synchronization` and `Data Subscription`, and it provides automatic, secure and 
reliable data transmission capabilities. InLong also 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.
 
 InLong (应龙) is a divine beast in Chinese mythology who guides the river into 
the sea, and it is regarded as a metaphor of the InLong system for reporting 
data streams.
 
@@ -64,7 +64,7 @@ Apache InLong offers a variety of features:
 
 
 ## When should I use InLong?
-InLong is based on MQ and aims to provide a one-stop, practice-tested module 
pluggable integration framework for massive data, based on this system, users 
can easily build stream-based data applications. It is suitable for 
environments that need to quickly build a data reporting platform, as well as 
an ultra-large-scale data reporting environment that InLong is very suitable 
for, and an environment that needs to automatically sort and land the reported 
data.
+InLong aims to provide a one-stop, full-scenario integration framework for 
massive data, users can easily build stream-based data applications. It 
supports `Data Ingestion`, `Data Synchronization` and `Data Subscription` at 
the same time, and is suitable for environments that need to quickly build a 
data reporting platform, as well as an ultra-large-scale data reporting 
environment that InLong is very suitable for, and an environment that needs to 
automatically sort and land the reported data.
 
 You can use InLong in the following ways:
 - Integrate InLong, manage data streams through 
[SDK](https://inlong.apache.org/docs/next/sdk/manager-sdk/example).
diff --git a/pom.xml b/pom.xml
index fcd668fb2d..5e051fd6f9 100644
--- a/pom.xml
+++ b/pom.xml
@@ -32,7 +32,7 @@
     <packaging>pom</packaging>
     <name>Apache InLong</name>
 
-    <description>InLong is a one-stop integration framework for massive data 
donated by Tencent to
+    <description>InLong is a one-stop, full-scenario 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.</description>

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