diff --git a/site/docs/blog/.authors.yml b/site/docs/blog/.authors.yml
index fc3b66a3896c..c21161d9fd21 100644
--- a/site/docs/blog/.authors.yml
+++ b/site/docs/blog/.authors.yml
@@ -18,3 +18,7 @@ authors:
name: Apache Iceberg PMC
description: Apache Iceberg PMC
avatar: assets/images/iceberg-logo-icon.png
+ yuya-ebihara:
+ name: Yuya Ebihara
+ description: Trino maintainer
+ avatar: https://github.com/ebyhr.png
diff --git a/site/docs/blog/posts/2026-01-21-iceberg-japan-meetup-4th.md b/site/docs/blog/posts/2026-01-21-iceberg-japan-meetup-4th.md
new file mode 100644
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--- /dev/null
+++ b/site/docs/blog/posts/2026-01-21-iceberg-japan-meetup-4th.md
@@ -0,0 +1,89 @@
+---
+date: 2026-01-21
+title: Iceberg Meetup Japan 4th Recap
+authors:
+ - yuya-ebihara
+categories:
+ - community
+---
+
+
+
+**Iceberg Meetup Japan #4 Recap**
+
+Apache Iceberg continues to gain momentum in Japan. In 2025, two Iceberg-related books were
+published [[1]](https://gihyo.jp/book/2025/978-4-297-15074-7)[[2]](https://book.impress.co.jp/books/1124101072), and our
+community meetups regularly attracted over 200 participants.
+
+To kick off 2026, we hosted the [Apache Iceberg Meetup Japan #4](https://iceberg.connpass.com/event/378396/) in January,
+featuring four presentations ranging from beginner introductions to advanced features.
+
+
+
+### Getting Started with Apache Iceberg
+
+
+
+Saki Kitaoka (Databricks) gave a clear introduction to Open Table Formats and Apache Iceberg. She explained how Iceberg
+brings reliability and manageability to data lakes by organizing metadata at the file level, enabling ACID transactions,
+time travel, and schema evolution.
+
+Her talk covered Iceberg’s architecture, highlighting its three logical layers and how metadata is managed for efficient
+read/write operations.
+
+**Key features:**
+
+- ACID reliability and historical data management
+- Metadata design for large-scale analytics
+- Open, engine-agnostic architecture
+
+### Apache Iceberg V3: Current Status and Migration
+
+
+
+Tomohiro Tanaka (AWS) discussed new data types and features in Iceberg V3, including unknown, variant, timestamp nanos,
+and geometry/geography types. He also covered Deletion Vectors, Row Lineage, Default Values, Multi-Argument Transforms,
+and Table Encryption.
+
+The session focused on the variant type and Deletion Vectors. With Variant, you can use `parse_json` at write time and
+`variant_get` at read time. V3 also improves efficiency by reducing Puffin file size during DELETE operations.
+
+Merge-on-Read performance gains depend on workload size, so benchmarking is recommended.
+
+### Iceberg Streaming Write at Repro
+
+
+
+Tomohiro Hashidate (Repro) shared his experience evaluating Iceberg for streaming writes under mixed workloads,
+including queries fetching millions of records and single-record lookups.
+
+Repro’s existing architecture uses Cassandra for write scalability and per-record updates, but it’s not ideal for bulk
+loads. After evaluating Hudi, they found Trino’s support limited and Spark compaction difficult to control.
+
+To replicate Cassandra queries, they created a Trino view that UNIONs Kafka and Iceberg connectors. Single queries
+performed well, but further evaluation is needed for high concurrency scenarios.
+
+### High-Performance Analytics on Iceberg with StarRocks
+
+
+
+Marko SUN (CelerData) introduced StarRocks, a high-performance MPP database for real-time analytics.
+
+He explained how
+metadata planning can be a bottleneck with Iceberg, and how StarRocks optimizes planning by switching between
+distributed and local strategies based on metadata size.