0626-blog-update-details

blog/apache-doris-4-1-iceberg-v3.md

@@ -1,9 +1,7 @@
 ---
     'title': 'Apache Doris 4.1 on Iceberg V3: Running the Full Lakehouse Lifecycle from One SQL Engine'
     'summary': 'Apache Doris 4.1 introduces comprehensive Iceberg V3 support, enabling reads, writes (UPDATE, DELETE, MERGE INTO), DDL operations, table maintenance, and diagnostics entirely through SQL without switching to other tools.'
     'description': 'Apache Doris 4.1 introduces comprehensive Iceberg V3 support, enabling reads, writes (UPDATE, DELETE, MERGE INTO), DDL operations, table maintenance, and diagnostics entirely through SQL without switching to other tools.'
-    'picked': "true"
-    'order': "3"    
     'date': '2026-5-22'
     'author': 'velodb.io · Rayner Chen'
     'externalLink': 'https://www.velodb.io/blog/apache-doris-4-1-on-iceberg-v3-full-lakehouse-lifecycle'

blog/apache-doris-and-polaris-per-user-identity-mode.md

@@ -0,0 +1,14 @@
+---
+    'title': 'Per-User Identity Mode: New Security Features with Apache Doris and Polaris'
+    'summary': 'Apache Doris 4.1 introduces per-user identity mode, forwarding each real user identity to Apache Polaris (Iceberg REST Catalog) instead of routing all queries through a shared service account.'
+    'description': 'Apache Doris 4.1 introduces per-user identity mode, forwarding each real user identity to Apache Polaris (Iceberg REST Catalog) instead of routing all queries through a shared service account.'
+    'date': '2026-6-5'
+    'author': 'velodb.io · Rayner Chen'
+    'externalLink': 'https://www.velodb.io/blog/apache-doris-and-polaris-per-user-identity-mode'
+    'tags': ['Tech Sharing']
+    "image": '/images/blogs/202606_Doris_Polaris_horizontal.jpeg'
+---
+import { BlogLink } from '../src/components/blogs/components/blog-link';
+import { SeeMore } from '../src/components/blogs/components/see-more';
+
+> <BlogLink rel="noopener noreferrer" target='_blank' href='https://www.velodb.io/blog/apache-doris-and-polaris-per-user-identity-mode'>Apache Doris 4.1 introduces per-user identity mode, forwarding each real user identity to Apache Polaris (Iceberg REST Catalog) instead of routing all queries through a shared service account. <SeeMore /></BlogLink>

blog/asof-join-benchmark-apache-doris-vs-clickhouse-and-duckdb.md

@@ -0,0 +1,16 @@
+---
+    'title': 'ASOF JOIN Benchmark: Apache Doris vs ClickHouse and DuckDB'
+    'summary': 'Apache Doris 4.1 outperforms ClickHouse and DuckDB on ASOF JOIN across all 11 benchmark scenarios.'
+    'description': 'Apache Doris 4.1 outperforms ClickHouse and DuckDB on ASOF JOIN across all 11 benchmark scenarios.'
+    'picked': "true"
+    'order': "1"
+    'date': '2026-6-11'
+    'author': 'velodb.io · Changle Zhao'
+    'externalLink': 'https://www.velodb.io/blog/asof-join-benchmark-apache-doris-vs-clickhouse-and-duckdb'
+    'tags': ['Tech Sharing']
+    "image": '/images/blogs/202606_ASOF_JOIN_Benchmark_horizontal.png'
+---
+import { BlogLink } from '../src/components/blogs/components/blog-link';
+import { SeeMore } from '../src/components/blogs/components/see-more';
+
+> <BlogLink rel="noopener noreferrer" target='_blank' href='https://www.velodb.io/blog/asof-join-benchmark-apache-doris-vs-clickhouse-and-duckdb'>Apache Doris 4.1 outperforms ClickHouse and DuckDB on ASOF JOIN across all 11 benchmark scenarios. <SeeMore /></BlogLink>

blog/chunking-embedding-cookbook.md

@@ -1,9 +1,7 @@
 ---
     'title': 'The Chunking and Embedding Cookbook for Production Context Engineering'
     'summary': 'This guide covers three critical decisions for production RAG systems: chunk shaping, embedding selection, and ANN index scaling, bridging the gap between demo retrieval and real-scale deployments.'
     'description': 'This guide covers three critical decisions for production RAG systems: chunk shaping, embedding selection, and ANN index scaling, bridging the gap between demo retrieval and real-scale deployments.'
-    'picked': "true"
-    'order': "4"    
     'date': '2026-5-15'
     'author': 'velodb.io · Tom Zhang'
     'externalLink': 'https://www.velodb.io/blog/the-chunking-and-embedding-cookbook-for-production-context-engineering'

blog/from-data-silos-to-context-silos.md

@@ -1,9 +1,7 @@
 ---
     'title': 'From Data Silos to Context Silos: What Database History Teaches Us About the AI Infrastructure Problem'
     'summary': 'The database industry is repeating a historical cycle where specialized systems create fragmentation that demands convergence. As AI agents become primary data consumers, organizations face a new challenge: context silos, where information exists but cannot be retrieved fast enough for autonomous systems to act effectively.'
     'description': 'The database industry is repeating a historical cycle where specialized systems create fragmentation that demands convergence. As AI agents become primary data consumers, organizations face a new challenge: context silos, where information exists but cannot be retrieved fast enough for autonomous systems to act effectively.'
-    'picked': "true"
-    'order': "2"    
     'date': '2026-5-9'
     'author': 'velodb.io · Kevin Shen'
     'externalLink': 'https://www.velodb.io/blog/from-data-silos-to-context-silos'

blog/how-we-built-production-vector-search-in-apache-doris.md

@@ -0,0 +1,16 @@
+---
+    'title': 'How We Built Production Vector Search in Apache Doris'
+    'summary': 'Apache Doris 4.1 adds more native ANN vector indexes, IVF and IVF_ON_DISK, directly inside its OLAP engine, reaching 900 QPS at 97% recall on VectorDBBench.'
+    'description': 'Apache Doris 4.1 adds more native ANN vector indexes, IVF and IVF_ON_DISK, directly inside its OLAP engine, reaching 900 QPS at 97% recall on VectorDBBench.'
+    'picked': "true"
+    'order': "3"
+    'date': '2026-5-28'
+    'author': 'velodb.io · Rayner Chen'
+    'externalLink': 'https://www.velodb.io/blog/how-we-built-production-vector-search-in-apache-doris'
+    'tags': ['Tech Sharing']
+    "image": '/images/blogs/202605_vector_search_header_horizontal.jpeg'
+---
+import { BlogLink } from '../src/components/blogs/components/blog-link';
+import { SeeMore } from '../src/components/blogs/components/see-more';
+
+> <BlogLink rel="noopener noreferrer" target='_blank' href='https://www.velodb.io/blog/how-we-built-production-vector-search-in-apache-doris'>Apache Doris 4.1 adds more native ANN vector indexes, IVF and IVF_ON_DISK, directly inside its OLAP engine, reaching 900 QPS at 97% recall on VectorDBBench. <SeeMore /></BlogLink>

blog/json-in-agent-observability-variant-and-inverted-indexes-in-apache-doris.md

@@ -0,0 +1,16 @@
+---
+    'title': 'Hybrid Modeling for JSON in Agent Observability: VARIANT and Inverted Indexes in Apache Doris'
+    'summary': 'VARIANT data type and native inverted indexes in Apache Doris offer a hybrid modeling approach that handles dynamic, schema-evolving agent observability logs with high performance.'
+    'description': 'VARIANT data type and native inverted indexes in Apache Doris offer a hybrid modeling approach that handles dynamic, schema-evolving agent observability logs with high performance.'
+    'picked': "true"
+    'order': "4"
+    'date': '2026-6-3'
+    'author': 'velodb.io · Max Li'
+    'externalLink': 'https://www.velodb.io/blog/json-in-agent-observability-variant-and-inverted-indexes-in-apache-doris'
+    'tags': ['Tech Sharing']
+    "image": '/images/blogs/202605_JSON_agent_observability_horizontal.jpeg'
+---
+import { BlogLink } from '../src/components/blogs/components/blog-link';
+import { SeeMore } from '../src/components/blogs/components/see-more';
+
+> <BlogLink rel="noopener noreferrer" target='_blank' href='https://www.velodb.io/blog/json-in-agent-observability-variant-and-inverted-indexes-in-apache-doris'>VARIANT data type and native inverted indexes in Apache Doris offer a hybrid modeling approach that handles dynamic, schema-evolving agent observability logs with high performance. <SeeMore /></BlogLink>

blog/netease-games-unified-doris-lakehouse.md

@@ -1,9 +1,7 @@
 ---
     'title': 'NetEase Games: From Elasticsearch, HBase, and ClickHouse to a Unified Apache Doris Lakehouse'
     'summary': 'NetEase Games consolidated six specialized data systems into Apache Doris across two phases, first unifying real-time analytics, then adding batch processing capabilities to create a lakehouse architecture serving 15 million daily queries.'
     'description': 'NetEase Games consolidated six specialized data systems into Apache Doris across two phases, first unifying real-time analytics, then adding batch processing capabilities to create a lakehouse architecture serving 15 million daily queries.'
-    'picked': "true"
-    'order': "1"    
     'date': '2026-5-22'
     'author': 'velodb.io · Biao Hu'
     'externalLink': 'https://www.velodb.io/blog/netease-games-from-elasticsearch-and-clickhouse-to-a-unified-apache-doris-lakehouse'

blog/why-ai-agents-need-real-time-analytics-and-hybrid-search.md

@@ -0,0 +1,16 @@
+---
+    'title': 'Why AI Agents Need Real-Time Analytics and Hybrid Search: The Data Infra for Production Agents'
+    'summary': 'AI agents need real-time analytics, not just vector search. Apache Doris unifies both capabilities, offering native hybrid search in one real-time engine built for agent workloads.'
+    'description': 'AI agents need real-time analytics, not just vector search. Apache Doris unifies both capabilities, offering native hybrid search in one real-time engine built for agent workloads.'
+    'picked': "true"
+    'order': "2"
+    'date': '2026-6-24'
+    'author': 'velodb.io · Matt Yi'
+    'externalLink': 'https://www.velodb.io/blog/why-ai-agents-need-real-time-analytics-and-hybrid-search'
+    'tags': ['Tech Sharing']
+    "image": '/images/blogs/202606_AI_Agent_Analytics_horizontal_2.jpeg'
+---
+import { BlogLink } from '../src/components/blogs/components/blog-link';
+import { SeeMore } from '../src/components/blogs/components/see-more';
+
+> <BlogLink rel="noopener noreferrer" target='_blank' href='https://www.velodb.io/blog/why-ai-agents-need-real-time-analytics-and-hybrid-search'>AI agents need real-time analytics, not just vector search. Apache Doris unifies both capabilities, offering native hybrid search in one real-time engine built for agent workloads. <SeeMore /></BlogLink>

src/components/recent-blogs/recent-blogs.data.ts

@@ -1,19 +1,19 @@
 export const RECENT_BLOGS_POSTS = [
     {
-        label: 'NetEase Games: From Elasticsearch, HBase, and ClickHouse to a Unified Apache Doris Lakehouse',
-        link: 'https://www.velodb.io/blog/netease-games-from-elasticsearch-and-clickhouse-to-a-unified-apache-doris-lakehouse',
+        label: 'ASOF JOIN Benchmark: Apache Doris vs ClickHouse and DuckDB',
+        link: 'https://www.velodb.io/blog/asof-join-benchmark-apache-doris-vs-clickhouse-and-duckdb',
     },
     {
-        label: 'From Data Silos to Context Silos: What Database History Teaches Us About the AI Infrastructure Problem',
-        link: 'https://www.velodb.io/blog/from-data-silos-to-context-silos',
+        label: 'Why AI Agents Need Real-Time Analytics and Hybrid Search: The Data Infra for Production Agents',
+        link: 'https://www.velodb.io/blog/why-ai-agents-need-real-time-analytics-and-hybrid-search',
     },
     {
-        label: 'Apache Doris 4.1 on Iceberg V3: Running the Full Lakehouse Lifecycle from One SQL Engine',
-        link: 'https://www.velodb.io/blog/apache-doris-4-1-on-iceberg-v3-full-lakehouse-lifecycle',
+        label: 'How We Built Production Vector Search in Apache Doris',
+        link: 'https://www.velodb.io/blog/how-we-built-production-vector-search-in-apache-doris',
     },
     {
-        label: 'The Chunking and Embedding Cookbook for Production Context Engineering',
-        link: 'https://www.velodb.io/blog/the-chunking-and-embedding-cookbook-for-production-context-engineering',
+        label: 'Hybrid Modeling for JSON in Agent Observability: VARIANT and Inverted Indexes in Apache Doris',
+        link: 'https://www.velodb.io/blog/json-in-agent-observability-variant-and-inverted-indexes-in-apache-doris',
     },
 
 ];

src/constant/newsletter.data.ts

@@ -1,30 +1,30 @@
 export const NEWSLETTER_DATA = [
     {
-        tags: ['Best Practice'],
-        title: "NetEase Games: From Elasticsearch, HBase, and ClickHouse to a Unified Apache Doris Lakehouse",
-        content: `NetEase Games consolidated six specialized data systems into Apache Doris across two phases, first unifying real-time analytics, then adding batch processing capabilities to create a lakehouse architecture serving 15 million daily queries.`,
-        to: 'https://www.velodb.io/blog/netease-games-from-elasticsearch-and-clickhouse-to-a-unified-apache-doris-lakehouse',
-        image: 'blogs/202605_netease_games_horizontal.jpg',
+        tags: ['Tech Sharing'],
+        title: "ASOF JOIN Benchmark: Apache Doris vs ClickHouse and DuckDB",
+        content: `Apache Doris 4.1 outperforms ClickHouse and DuckDB on ASOF JOIN across all 11 benchmark scenarios.`,
+        to: 'https://www.velodb.io/blog/asof-join-benchmark-apache-doris-vs-clickhouse-and-duckdb',
+        image: 'blogs/202606_ASOF_JOIN_Benchmark_horizontal.png',
     },
     {
         tags: ['Tech Sharing'],
-        title: "From Data Silos to Context Silos: What Database History Teaches Us About the AI Infrastructure Problem",
-        content: `The database industry is repeating a historical cycle where specialized systems create fragmentation that demands convergence. As AI agents become primary data consumers, organizations face a new challenge: context silos, where information exists but cannot be retrieved fast enough for autonomous systems to act effectively.`,
-        to: 'https://www.velodb.io/blog/from-data-silos-to-context-silos',
-        image: 'blogs/202605_context_silo_horizontal.png',
+        title: "Why AI Agents Need Real-Time Analytics and Hybrid Search: The Data Infra for Production Agents",
+        content: `AI agents need real-time analytics, not just vector search. Apache Doris unifies both capabilities, offering native hybrid search in one real-time engine built for agent workloads.`,
+        to: 'https://www.velodb.io/blog/why-ai-agents-need-real-time-analytics-and-hybrid-search',
+        image: 'blogs/202606_AI_Agent_Analytics_horizontal_2.jpeg',
     },
     {
         tags: ['Tech Sharing'],
-        title: "Apache Doris 4.1 on Iceberg V3: Running the Full Lakehouse Lifecycle from One SQL Engine",
-        content: `Apache Doris 4.1 introduces comprehensive Iceberg V3 support, enabling reads, writes (UPDATE, DELETE, MERGE INTO), DDL operations, table maintenance, and diagnostics entirely through SQL without switching to other tools.`,
-        to: 'https://www.velodb.io/blog/apache-doris-4-1-on-iceberg-v3-full-lakehouse-lifecycle',
-        image: 'blogs/202605_Iceberg_v3_horizontal.jpg',
+        title: "How We Built Production Vector Search in Apache Doris",
+        content: `Apache Doris 4.1 adds more native ANN vector indexes, IVF and IVF_ON_DISK, directly inside its OLAP engine, reaching 900 QPS at 97% recall on VectorDBBench.`,
+        to: 'https://www.velodb.io/blog/how-we-built-production-vector-search-in-apache-doris',
+        image: 'blogs/202605_vector_search_header_horizontal.jpeg',
     },
     {
         tags: ['Tech Sharing'],
-        title: "The Chunking and Embedding Cookbook for Production Context Engineering",
-        content: `This guide covers three critical decisions for production RAG systems: chunk shaping, embedding selection, and ANN index scaling, bridging the gap between demo retrieval and real-scale deployments.`,
-        to: 'https://www.velodb.io/blog/the-chunking-and-embedding-cookbook-for-production-context-engineering',
-        image: 'blogs/20260515_chunking_horizontal.png',
+        title: "Hybrid Modeling for JSON in Agent Observability: VARIANT and Inverted Indexes in Apache Doris",
+        content: `VARIANT data type and native inverted indexes in Apache Doris offer a hybrid modeling approach that handles dynamic, schema-evolving agent observability logs with high performance.`,
+        to: 'https://www.velodb.io/blog/json-in-agent-observability-variant-and-inverted-indexes-in-apache-doris',
+        image: 'blogs/202605_JSON_agent_observability_horizontal.jpeg',
     },
 ];