Docs: Add blog of Iceberg Meetup Japan 4th Recap

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

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
+---
+
+<!--
+ - Licensed to the Apache Software Foundation (ASF) under one or more
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+ -   http://www.apache.org/licenses/LICENSE-2.0
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+ - distributed under the License is distributed on an "AS IS" BASIS,
+ - WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ - See the License for the specific language governing permissions and
+ - limitations under the License.
+ -->
+
+**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.
+
+<!-- more -->
+
+### Getting Started with Apache Iceberg
+
+<iframe class="speakerdeck-iframe" frameborder="0" src="https://speakerdeck.com/player/23f7d16789e34adfb5e96d36485a3d2a" title="[Iceberg Meetup #4] ゼロからはじめる: Apache Icebergとはなにか？ / Apache Iceberg for Beginners" allowfullscreen="true" style="border: 0px; background: padding-box padding-box rgba(0, 0, 0, 0.1); margin: 0px; padding: 0px; border-radius: 6px; box-shadow: rgba(0, 0, 0, 0.2) 0px 5px 40px; width: 100%; height: auto; aspect-ratio: 560 / 315;" data-ratio="1.7777777777777777"></iframe>
+
+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
+
+<iframe class="speakerdeck-iframe" frameborder="0" src="https://speakerdeck.com/player/db6c8369596a44c79c9bd6f085bd9ed6" title="Apache Iceberg V3 and migration to V3" allowfullscreen="true" style="border: 0px; background: padding-box padding-box rgba(0, 0, 0, 0.1); margin: 0px; padding: 0px; border-radius: 6px; box-shadow: rgba(0, 0, 0, 0.2) 0px 5px 40px; width: 100%; height: auto; aspect-ratio: 560 / 315;" data-ratio="1.7777777777777777"></iframe>
+
+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
+
+<iframe class="speakerdeck-iframe" frameborder="0" src="https://speakerdeck.com/player/468c4310bb5c43fb8786dc46fd996dc0" title="ReproでのicebergのStreaming Writeの検証と実運用にむけた取り組み" allowfullscreen="true" style="border: 0px; background: padding-box padding-box rgba(0, 0, 0, 0.1); margin: 0px; padding: 0px; border-radius: 6px; box-shadow: rgba(0, 0, 0, 0.2) 0px 5px 40px; width: 100%; height: auto; aspect-ratio: 560 / 315;" data-ratio="1.7777777777777777"></iframe>
+
+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
+
+<iframe class="speakerdeck-iframe" frameborder="0" src="https://speakerdeck.com/player/22380500964947528656234bfbff1a98" title="StarRocks Introduction for Iceberg Meetup Japan #4" allowfullscreen="true" style="border: 0px; background: padding-box padding-box rgba(0, 0, 0, 0.1); margin: 0px; padding: 0px; border-radius: 6px; box-shadow: rgba(0, 0, 0, 0.2) 0px 5px 40px; width: 100%; height: auto; aspect-ratio: 560 / 315;" data-ratio="1.7777777777777777"></iframe>
+
+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.