EMsnap commented on code in PR #737:
URL: https://github.com/apache/inlong-website/pull/737#discussion_r1147093997


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blog/2023-03-23-release-1.6.0.md:
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+---
+title: Release 1.6.0
+author: Charles Zhang
+author_url: https://github.com/dockerzhang
+author_image_url: https://avatars.githubusercontent.com/u/18047329?v=4
+tags: [Apache InLong, Version]
+---
+
+Apache InLong recently released version 1.6.0, which closed about 202+ issues, 
including 11+ major features and 80+ optimizations. Mainly completed the 
addition of Kudu data stream, improvement of Redis data stream, the addition of 
MQ cache cluster selector strategy, optimization of Audit ID allocation rules, 
the addition of data node connection testing, optimization of Sort Audit 
reconciliation benchmark time, and expansion of Audit support for using Kafka 
to cache audit data.
+<!--truncate-->
+
+## About Apache InLong
+As the industry's first one-stop open-source massive data integration 
framework, Apache InLong provides automatic, safe, reliable, and 
high-performance data transmission capabilities to facilitate businesses to 
build stream-based data analysis, modeling, and applications quickly. At 
present, InLong is widely used in various industries such as advertising, 
payment, social networking, games, artificial intelligence, etc., serving 
thousands of businesses, among which the scale of high-performance scene data 
exceeds 1 trillion lines per day, and the scale of high-reliability scene data 
exceeds 10 trillion lines per day.
+
+The core keywords of InLong project positioning are "one-stop" and  "massive 
data". For "one-stop", we hope to shield technical details, provide complete 
data integration and support services, and implement out-of-the-box; With its 
advantages, such as multi-cluster management, it can stably support 
larger-scale data volumes based on trillions lines per day.

Review Comment:
   multiple blanks



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