[incubator-inlong] branch master updated: [INLONG-2103] update the definition of Apache InLong (#2104)

[do...@apache.org]

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dockerzhang pushed a commit to branch master
in repository https://gitbox.apache.org/repos/asf/incubator-inlong.git


The following commit(s) were added to refs/heads/master by this push:
     new 00d311e  [INLONG-2103] update the definition of Apache InLong (#2104)
00d311e is described below

commit 00d311eaefcf56bcdb05359db02c8939d3a70077
Author: dockerzhang <do...@apache.org>
AuthorDate: Wed Jan 5 11:00:19 2022 +0800

    [INLONG-2103] update the definition of Apache InLong (#2104)
    
    Co-authored-by: dockerzhang <do...@tencent.com>
---
 .asf.yaml | 6 +++---
 README.md | 4 ++--
 pom.xml   | 2 +-
 3 files changed, 6 insertions(+), 6 deletions(-)

diff --git a/.asf.yaml b/.asf.yaml
index 1b9e315..c54cc7b 100644
--- a/.asf.yaml
+++ b/.asf.yaml
@@ -19,13 +19,13 @@
 # of the project and make sure to discuss the changes with dev@ before committing.
 
 github:
-  description: "Apache InLong - a one-stop data ingestion platform"
+  description: "Apache InLong - a one-stop data integration framework"
   homepage: https://inlong.apache.org/
   labels:
     - inlong
     - one-stop-service
-    - data-ingestion
-    - messaging
+    - data-integration
+    - framework
     - data-streaming
     - event-streaming
   features:
diff --git a/README.md b/README.md
index c63a0f2..069098a 100644
--- a/README.md
+++ b/README.md
@@ -39,7 +39,7 @@
 - [License](#license)
 
 # What is Apache InLong?
-[Apache InLong](https://inlong.apache.org)(incubating) is a one-stop data ingestion platform 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)(incubating) 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.
 
 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.
 
@@ -59,7 +59,7 @@ 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 data ingestion service platform, 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 is based on MQ and aims to provide a one-stop, practice-tested module pluggable data integration framework, 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 is only a one-stop data reporting pipeline platform. It cannot be used as a persistent data storage, nor does it support complex business logic processing on data streams.
 
diff --git a/pom.xml b/pom.xml
index e835797..e8e5112 100644
--- a/pom.xml
+++ b/pom.xml
@@ -33,7 +33,7 @@
     <version>0.13.0-incubating-SNAPSHOT</version>
     <name>Apache InLong</name>
 
-    <description>InLong is a one-stop data ingestion platform donated by Tencent to the Apache community.
+    <description>InLong 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.
     </description>