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The following commit(s) were added to refs/heads/asf-site by this push:
     new 3882f99a6b87 chore: update links (#17458)
3882f99a6b87 is described below

commit 3882f99a6b877680e566f15d30231337d573b732
Author: Shiyan Xu <[email protected]>
AuthorDate: Tue Dec 2 13:39:59 2025 -0600

    chore: update links (#17458)
---
 ...ive-into-hudis-indexing-subsystem-part-1-of-2.md |   2 +-
 ...-12-01-apache-hudi-JD-meetup-asia-2025-recap.md} |  20 ++++++++++----------
 website/src/components/BlogsSlider/index.js         |   2 +-
 website/src/components/HomepageFeatures/index.js    |   6 +++---
 .../jdpost-image1.png                               | Bin
 .../jdpost-image2.jpg                               | Bin
 .../jdpost-image3.jpg                               | Bin
 .../jdpost-image4.jpg                               | Bin
 .../jdpost-image5.jpg                               | Bin
 .../jdpost-image6.jpg                               | Bin
 .../jdpost-image7.jpg                               | Bin
 11 files changed, 15 insertions(+), 15 deletions(-)

diff --git 
a/website/blog/2025-10-29-deep-dive-into-hudis-indexing-subsystem-part-1-of-2.md
 
b/website/blog/2025-10-29-deep-dive-into-hudis-indexing-subsystem-part-1-of-2.md
index 9377a32609c3..eac1c111e83a 100644
--- 
a/website/blog/2025-10-29-deep-dive-into-hudis-indexing-subsystem-part-1-of-2.md
+++ 
b/website/blog/2025-10-29-deep-dive-into-hudis-indexing-subsystem-part-1-of-2.md
@@ -13,7 +13,7 @@ tags:
 
 For decades, databases have relied on indexes—specialized data structures—to 
dramatically improve read and write performance by quickly locating specific 
records. Apache Hudi extends this fundamental principle to the data lakehouse 
with a unique and powerful approach. Every Hudi table contains a self-managed 
metadata table that functions as an indexing subsystem, enabling efficient data 
skipping and fast record lookups across a wide range of read and write 
scenarios.
 
-This two-part series dives into Hudi’s indexing subsystem. Part 1 explains the 
internal layout and data-skipping capabilities. [part 
2](https://hudi.apache.org/blog/2025/11/12/deep-dive-into-hudis-indexing-subsystem-part-2-of-2/)
 covers advanced features—record, secondary, and expression indexes—and 
asynchronous index maintenance. By the end, you’ll know how to leverage Hudi’s 
multimodal index to build more efficient lakehouse tables.
+This two-part series dives into Hudi’s indexing subsystem. Part 1 explains the 
internal layout and data-skipping capabilities. [Part 
2](https://hudi.apache.org/blog/2025/11/12/deep-dive-into-hudis-indexing-subsystem-part-2-of-2/)
 covers advanced features—record, secondary, and expression indexes—and 
asynchronous index maintenance. By the end, you’ll know how to leverage Hudi’s 
multimodal index to build more efficient lakehouse tables.
 
 ## The Metadata Table
 
diff --git a/website/blog/2025-12-01-apache-hudi-meetup-asia-2024-recap.md 
b/website/blog/2025-12-01-apache-hudi-JD-meetup-asia-2025-recap.md
similarity index 93%
rename from website/blog/2025-12-01-apache-hudi-meetup-asia-2024-recap.md
rename to website/blog/2025-12-01-apache-hudi-JD-meetup-asia-2025-recap.md
index b07d56a57310..18ff6590e2fe 100644
--- a/website/blog/2025-12-01-apache-hudi-meetup-asia-2024-recap.md
+++ b/website/blog/2025-12-01-apache-hudi-JD-meetup-asia-2025-recap.md
@@ -3,7 +3,7 @@ title: "Next Generation Lakehouse: New Engine for the 
Intelligent Future | Apach
 excerpt: "A comprehensive recap of the Apache Hudi Meetup Asia held at JD.com 
headquarters, featuring insights from Onehouse, JD.com, Kuaishou, and Huawei on 
Hudi 1.1, AI-native architectures, and production optimizations."
 author: "Team at JD.com"
 category: blog
-image: 
/assets/images/blog/2025-12-01-apache-hudi-meetup-asia-2024-recap/jdpost-image7.jpg
+image: 
/assets/images/blog/2025-12-01-apache-hudi-JD-meetup-asia-2025-recap/jdpost-image7.jpg
 tags:
   - hudi
   - meetup
@@ -19,7 +19,7 @@ _This blog was translated from the [original blog in 
Chinese](https://mp.weixin.
 
 Recently, the Apache Hudi Meetup Asia, hosted by JD.com, was successfully held 
at JD.com Group headquarters. Four technical experts from Onehouse, JD.com, 
Kuaishou, and Huawei gathered together, not only bringing a preview of Apache 
Hudi release 1.1, but also sharing their unique approaches to building data 
lakehouses. From AI scenario support to real-time data processing and cost 
optimization, each topic directly addressed the pain points that data engineers 
care about most.
 
-## Hudi Community Leader Joins Remotely
+## Hudi Community Leader Joined Remotely
 
 First, Vinoth Chandar, CEO & Founder of Onehouse and Apache Hudi PMC Chair, 
delivered the opening remarks via video. He stated that after eight years of 
development, Hudi has become an important cornerstone in the data lake domain, 
and its vision has transformed into widely recognized achievements in the 
industry. The 1.0 version released last year marked the project's entry into a 
mature stage, bringing many database-like capabilities to the lakehouse.
 
@@ -27,7 +27,7 @@ Currently, the community is steadily advancing the 1.x series 
of versions, focus
 
 Vinoth specially thanked JD.com for its significant contributions to Apache 
Hudi. Among the top 100 contributors, 6 were from JD.com. Finally, he also 
invited more developers to join this vibrant community to jointly promote 
innovation and development in data infrastructure.
 
-![image 
1](/assets/images/blog/2025-12-01-apache-hudi-meetup-asia-2024-recap/jdpost-image1.png)
+![image 
1](/assets/images/blog/2025-12-01-apache-hudi-JD-meetup-asia-2025-recap/jdpost-image1.png)
 
 ## JD Retail: Data Lake Technical Challenges and Outlook
 
@@ -39,7 +39,7 @@ At the AI level, with the arrival of the multimodal era, 
traditional solutions t
 
 The industry is looking forward to building a storage foundation through 
open-source technologies like Apache Hudi that can uniformly carry batch 
processing, stream computing, data analysis, and AI workloads.
 
-![image 
2](/assets/images/blog/2025-12-01-apache-hudi-meetup-asia-2024-recap/jdpost-image2.jpg)
+![image 
2](/assets/images/blog/2025-12-01-apache-hudi-JD-meetup-asia-2025-recap/jdpost-image2.jpg)
 
 ## Apache Hudi 1.1 Preview and AI-Native Lakehouse Evolution
 
@@ -51,7 +51,7 @@ In the then-upcoming 1.1 release (now released), Hudi has 
achieved several impor
 
 Facing new challenges brought by the AI era, Hudi is actively building a 
native AI data foundation. By supporting unstructured data storage, optimizing 
column group structures for multimodal data, providing built-in vector indexing 
capabilities, and building a unified storage layer that supports transactions 
and version control, Hudi is committed to providing highly real-time, 
traceable, and easily extensible data support for AI workflows. This series of 
evolutions will propel Apache Hud [...]
 
-![image 
3](/assets/images/blog/2025-12-01-apache-hudi-meetup-asia-2024-recap/jdpost-image3.jpg)
+![image 
3](/assets/images/blog/2025-12-01-apache-hudi-JD-meetup-asia-2025-recap/jdpost-image3.jpg)
 
 ## Latest Architecture Evolution of Apache Hudi at JD.com
 
@@ -67,9 +67,9 @@ JD had deeply integrated these capabilities with business 
scenarios, applying th
 
 While promoting self-developed technology, JD also actively gave back to the 
open-source community, with a total of 109 contributed and merged PRs. In the 
future, the team will continue to deepen Hudi's application in the real-time 
data lake domain, providing stronger data support capabilities for business 
innovation.
 
-![image 
4](/assets/images/blog/2025-12-01-apache-hudi-meetup-asia-2024-recap/jdpost-image4.jpg)
+![image 
4](/assets/images/blog/2025-12-01-apache-hudi-JD-meetup-asia-2025-recap/jdpost-image4.jpg)
 
-## How Kuaishou's Real-time Lake Ingestion Empowers BI & AI Scenario 
Architecture Upgrade
+## How Kuaishou's Real-time Lake Ingestion Empowered BI & AI Scenario 
Architecture Upgrade
 
 In the session "How Kuaishou's Real-time Lake Ingestion Empowers BI & AI 
Scenario Architecture Upgrade," Wang Zeyu, Data Architecture R&D Engineer at 
Kuaishou, introduced Kuaishou's complete evolution path and practical 
experience in building a real-time data lake based on Apache Hudi.
 
@@ -79,7 +79,7 @@ At the AI storage architecture level, Kuaishou built a 
unified stream-batch data
 
 In the future, Kuaishou will continue to improve the data lake's service 
capabilities in training, retrieval, analysis, and other multi-scenarios, 
promoting the evolution of the data lake toward a more intelligent and unified 
direction. Kuaishou's practice fully proves that the real-time data lake 
architecture based on Hudi can effectively support the modernization and 
upgrade needs of large-scale BI and AI scenarios.
 
-![image 
5](/assets/images/blog/2025-12-01-apache-hudi-meetup-asia-2024-recap/jdpost-image5.jpg)
+![image 
5](/assets/images/blog/2025-12-01-apache-hudi-JD-meetup-asia-2025-recap/jdpost-image5.jpg)
 
 ## Deep Optimization and AI Exploration of Apache Hudi on Huawei Cloud
 
@@ -93,10 +93,10 @@ Hudi Native built a high-performance IO acceleration layer 
by rewriting Parquet
 
 In ecosystem integration and AI exploration, Huawei built a management 
architecture supporting multimodal data. By using lake table formats to manage 
metadata of unstructured data, with actual files stored in object storage, it 
ensured ACID properties while avoiding data redundancy. At the same time, it 
integrated LanceDB to provide efficient vector retrieval capabilities, 
providing comprehensive data infrastructure support for AI application 
scenarios such as document retrieval and inte [...]
 
-![image 
6](/assets/images/blog/2025-12-01-apache-hudi-meetup-asia-2024-recap/jdpost-image6.jpg)
+![image 
6](/assets/images/blog/2025-12-01-apache-hudi-JD-meetup-asia-2025-recap/jdpost-image6.jpg)
 
 ## Conclusion
 
 This meetup made us believe that the vast ocean of data lakehouses could not 
be separated from the "collective effort" of the open-source community and 
enterprises. Those technologies tempered on the business battlefield ultimately 
gave back as nutrients nourishing the entire ecosystem. This may be the purest 
romance of technology: making complex things simple and making the impossible 
possible. The road ahead is full of imagination, and together, we are shaping a 
more elegant and powerf [...]
 
-![image 
7](/assets/images/blog/2025-12-01-apache-hudi-meetup-asia-2024-recap/jdpost-image7.jpg)
+![image 
7](/assets/images/blog/2025-12-01-apache-hudi-JD-meetup-asia-2025-recap/jdpost-image7.jpg)
diff --git a/website/src/components/BlogsSlider/index.js 
b/website/src/components/BlogsSlider/index.js
index b942c80421d4..07fb58243f16 100644
--- a/website/src/components/BlogsSlider/index.js
+++ b/website/src/components/BlogsSlider/index.js
@@ -102,7 +102,7 @@ const BlogsSlider = () => {
           />
         </div>
         <div className={styles.blogViewAllBtnWrapper}>
-          <LinkButton to="/blog" className={styles.blogBtn}>
+          <LinkButton to="/learn/blog" className={styles.blogBtn}>
             View All
           </LinkButton>
         </div>
diff --git a/website/src/components/HomepageFeatures/index.js 
b/website/src/components/HomepageFeatures/index.js
index f12a1cb4fd1e..406d64059d6d 100644
--- a/website/src/components/HomepageFeatures/index.js
+++ b/website/src/components/HomepageFeatures/index.js
@@ -67,10 +67,10 @@ const HomepageFeatures = () => {
     },
     {
       icon: MultiModalIndexes,
-      title: "Query acceleration through multi-modal indexes.",
+      title: "Query acceleration through multimodal indexes.",
       description:
-        "Experience faster write transactions on huge/wide tables & faster 
query performance with first-of-its kind multi-modal indexing subsystem.",
-      link: 
"/blog/2022/05/17/Introducing-Multi-Modal-Index-for-the-Lakehouse-in-Apache-Hudi",
+        "Experience faster write transactions on huge/wide tables & faster 
query performance with first-of-its kind multimodal indexing subsystem.",
+      link: 
"/blog/2025/10/29/deep-dive-into-hudis-indexing-subsystem-part-1-of-2/",
     },
     {
       icon: SchemaEvolution,
diff --git 
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