update and address comments

Author: wuchong

website/blog/releases/0.8.md

@@ -1,22 +1,22 @@
 ---
-title: "Apache Fluss 0.8: Streaming Lakehouse with Iceberg/Lance"
+title: "Announcing Apache Fluss 0.8: Streaming Lakehouse for Data + AI"
 sidebar_label: "Announcing Apache Fluss 0.8"
 authors: [giannis, jark]
-date: 2025-10-30
+date: 2025-11-08
 tags: [releases]
 ---
 
 ![Banner](../assets/0.8/banner.jpg)
 
-🌊 We are excited to announce the official release of **Apache Fluss 0.8**!
+🌊 We are excited to announce the official release of **Apache Fluss 0.8 (incubating)**!
 
-This marks the first Apache Software Foundation (ASF) release for Apache Fluss (incubating), marking a significant milestone in our journey to provide a robust streaming storage platform for real-time analytics.
+This is our first release under the incubator of the Apache Software Foundation, marking a significant milestone in our journey to provide a robust streaming storage platform for real-time analytics.
 
-Over the past four months, the community has made tremendous progress, delivering 390+ commits that push the boundaries of the Streaming Lakehouse ecosystem. This release introduces deeper integrations, performance breakthroughs, and next-generation stream processing capabilities, including:
+Over the past four months, the community has made tremendous progress, delivering nearly 400 commits that push the boundaries of the Streaming Lakehouse ecosystem. This release introduces deeper integrations, performance breakthroughs, and next-generation stream processing capabilities, including:
 
 * 🔗 Tighter integration with Apache Flink for seamless real-time processing. 
 * 🧊 Enhanced Streaming Lakehouse capabilities with full support for [Apache Iceberg](https://iceberg.apache.org/) and [Lance](https://lancedb.github.io/lance/)
-* ⚡ Introduction of [Delta Joins](https://cwiki.apache.org/confluence/display/FLINK/FLIP-486%3A+Introduce+A+New+DeltaJoin), a game-changing innovation that redefines efficiency in stream processing by minimizing state and maximizing speed.
+* ⚡ Introduction of [Delta Joins](https://cwiki.apache.org/confluence/display/FLINK/FLIP-486%3A+Introduce+A+New+DeltaJoin) with Flink, a game-changing innovation that redefines efficiency in stream processing by minimizing state and maximizing speed.
 
 Apache Fluss 0.8 marks the beginning of a new era in streaming:
 **real-time**, **unified**, and **zero-state**, purpose-built to power the next generation of data platforms with **low-latency performance**, **scalability**, and **architectural simplicity**.
@@ -51,11 +51,11 @@ datalake.iceberg.type: hadoop
 datalake.iceberg.warehouse: /path/to/iceberg
 ```
 
-You can find more detailed instructions in the [documentation](/docs/next/streaming-lakehouse/integrate-data-lakes/iceberg/).
+You can find more detailed instructions in the [Iceberg Lakehouse documentation](/docs/next/streaming-lakehouse/integrate-data-lakes/iceberg/).
 
 ## Real-Time Multimodal AI Analytics with Lance
 
-Another major enhancement in Fluss 0.8 is the addition of **Streaming Lakehouse support for [Lance](https://github.com/lancedb/lance)** ([FIP-5](https://cwiki.apache.org/confluence/display/FLUSS/FIP-5%3A+Support+tiering+Fluss+data+to+Lance),
+Another major enhancement in Fluss 0.8 is the addition of **Streaming Lakehouse support for [Lance](https://github.com/lancedb/lance)** ([FIP-5](https://cwiki.apache.org/confluence/display/FLUSS/FIP-5%3A+Support+tiering+Fluss+data+to+Lance)),
 a modern columnar and vector-native data format designed for AI and machine learning workloads.
 This integration extends Apache Fluss towards being a real-time ingestion platform for multi-modal data & AI,
 not just traditional tabular streams, but also embeddings, vectors, and unstructured features used in AI systems.
@@ -80,7 +80,7 @@ datalake.lance.access_key_id: <access_key_id>
 datalake.lance.secret_access_key: <secret_access_key>
 ```
 
-See the [LanceDB blog post](https://lancedb.com/blog/fluss-integration/) for the full integration. You also can find more detailed instructions in the [documentation](/docs/next/streaming-lakehouse/integrate-data-lakes/lance/).
+See the [LanceDB blog post](https://lancedb.com/blog/fluss-integration/) for the full integration. You also can find more detailed instructions in the [Lance Lakehouse documentation](/docs/next/streaming-lakehouse/integrate-data-lakes/lance/).
 
 ## Flink 2.1
 
@@ -102,7 +102,7 @@ Below is a performance comparison (CPU, memory, state size, checkpoint interval)
 ![](../assets/taobao_practice/performance_delta2.png)
 
 
-TODO: add documentation link
+You can find more detailed instructions in the [Delta Join documentation](/docs/next/engine-flink/delta-joins/).
 
 ### Materialized Table
 
@@ -135,8 +135,7 @@ WITH(
 );
 ```
 
-TODO: add documentation link
-
+You can find more detailed instructions in the [Materialized Table documentation](/docs/next/engine-flink/ddl/#materialized-table).
 
 ## Stability
 
@@ -145,7 +144,7 @@ Through continuous validation across multiple business units within Alibaba Grou
 These improvements substantially enhance Fluss’s robustness in mission-critical streaming use cases.
 
 Key improvements include:
-- **Graceful Shutdown**: Introduced a graceful shutdown mechanism for TabletServers. During shutdown, leadership is proactively migrated before termination, ensuring that read/write latency remains unaffected by node decommissioning.
+- **[Graceful Shutdown](/docs/next/maintenance/operations/graceful-shutdown/)**: Introduced a graceful shutdown mechanism for TabletServers. During shutdown, leadership is proactively migrated before termination, ensuring that read/write latency remains unaffected by node decommissioning.
 - **Accelerated Coordinator Event Processing**: Optimized the Coordinator’s event handling mechanism through asynchronous processing and batched ZooKeeper operations. As a result, all events are now processed in milliseconds.
 - **Faster Coordinator Recovery**: Parallelized initialization cuts Coordinator startup time from 10 minutes to just 20 seconds in production-scale benchmarks, this dramatically improves service availability and recovery speed.
 - **Optimized Server Metrics**: Refined metric granularity and reporting logic to reduce telemetry volume by 90% while preserving full observability.
@@ -155,21 +154,41 @@ With these foundational stability improvements, Fluss 0.8 is now production-read
 
 ## Dynamic Configuration
 
+Starting with Fluss version 0.8, certain **cluster-level configurations** and **table-level configurations** can be updated dynamically, without requiring a cluster restart or table recreation. This enables operators and developers to adjust system behavior in real time, improving operational agility and minimizing downtime.
+
 ### Dynamic Cluster Configs
 
-TODO: need feature documentation
+Fluss now supports runtime updates for cluster configuration parameters. These changes take effect immediately across the cluster after being applied through the API.
+
+```java title="Java Client"
+Admin admin = connection.getAdmin();
+Collection<AlterConfig> configsToUpdate = Arrays.asList(
+    new AlterConfig("datalake.format", "paimon", AlterConfigOpType.SET));
+admin.alterClusterConfigs(configsToUpdate)
+```
 
 ### Dynamic Table Configs
 
-TODO: need feature documentation
+Fluss now supports update options dynamically on a table using the `ALTER TABLE ... SET` statement. This supports all the client-wise options (like `scan.startup.mode`) and some storage-wise options (like `table.datalake.enabled`).
+
+```sql title="Flink SQL"
+-- Enable lakehouse storage for the given table
+ALTER TABLE my_table SET ('table.datalake.enabled' = 'true');
+```
+
+When you issue a `ALTER TABLE ... SET` command to update storage options on a table, the Fluss cluster validates and applies the new configuration immediately. The updated settings are propagated to all TabletServers and CoordinatorServer components, ensuring consistent behavior going forward.
+
+This capability is especially useful for tuning performance, adapting to changing data patterns, or complying with evolving data governance requirements—all without service interruption.
+
+You can find more detailed instructions in the [Updating Configs documentation](/docs/next/maintenance/operations/updating-configs/).
 
 ## Helm Charts
 
 This release also introduced Helm Charts. With this addition, users can now deploy and manage a full Fluss cluster using [Helm](https://helm.sh/).
 The Helm chart simplifies provisioning, upgrades, and scaling by packaging configuration, manifests, and dependencies into a single, versioned release.
 This should help users running Fluss on Kubernetes faster, more reliably, and with easier integration into existing CI/CD and observability setups, significantly lowering the barrier for teams adopting Fluss in production.
 
-TODO: add documentation link
+You can find more detailed instructions in the [Deploying with Helm documentation](/docs/next/install-deploy/deploying-with-helm/).
 
 ## Ecosystem