[docs] Update datalake related doc for support iceberg and lance (#1686)

Author: caozhen1937

fluss-common/src/main/java/org/apache/fluss/config/ConfigOptions.java

@@ -1299,7 +1299,8 @@ public class ConfigOptions {
                     .enumType(DataLakeFormat.class)
                     .noDefaultValue()
                     .withDescription(
-                            "The data lake format of the table specifies the tiered Lakehouse storage format, such as Paimon, Iceberg, DeltaLake, or Hudi. Currently, only `paimon` is supported. "
+                            "The data lake format of the table specifies the tiered Lakehouse storage format. Currently, supported formats are `paimon`, `iceberg`, and `lance`. "
+                                    + "In the future, more kinds of data lake format will be supported, such as DeltaLake or Hudi. "
                                     + "Once the `table.datalake.format` property is configured, Fluss adopts the key encoding and bucketing strategy used by the corresponding data lake format. "
                                     + "This ensures consistency in key encoding and bucketing, enabling seamless **Union Read** functionality across Fluss and Lakehouse. "
                                     + "The `table.datalake.format` can be pre-defined before enabling `table.datalake.enabled`. This allows the data lake feature to be dynamically enabled on the table without requiring table recreation. "
@@ -1646,8 +1647,8 @@ public class ConfigOptions {
                     .enumType(DataLakeFormat.class)
                     .noDefaultValue()
                     .withDescription(
-                            "The datalake format used by Fluss to be as lake storage, such as Paimon, Iceberg, Hudi. "
-                                    + "Now, only support Paimon.");
+                            "The datalake format used by of Fluss to be as lakehouse storage. Currently, supported formats are Paimon, Iceberg, and Lance. "
+                                    + "In the future, more kinds of data lake format will be supported, such as DeltaLake or Hudi.");
 
     // ------------------------------------------------------------------------
     //  ConfigOptions for fluss kafka

website/docs/engine-flink/options.md

@@ -116,8 +116,9 @@ We have listed them in the table below the figure.
                by query engines such as Flink, Spark, StarRocks, Trino.
             </td>
             <td>
-            <li>[Paimon](maintenance/tiered-storage/lakehouse-storage.md)</li>
-            <li>[Iceberg (Roadmap)](/roadmap/)</li>
+                <li>[Paimon](streaming-lakehouse/integrate-data-lakes/paimon.md)</li>
+                <li>[Iceberg](streaming-lakehouse/integrate-data-lakes/iceberg.md)</li>
+                <li>[Lance](streaming-lakehouse/integrate-data-lakes/lance.md)</li>
             </td>
         </tr>
         <tr>

website/docs/install-deploy/overview.md

@@ -164,9 +164,9 @@ during the Fluss cluster working.
 
 ## Lakehouse
 
-| Option          | Type | Default | Description                                                                                                               |
-|-----------------|------|---------|---------------------------------------------------------------------------------------------------------------------------|
-| datalake.format | Enum | (None)  | The datalake format used by of Fluss to be as lakehouse storage, such as Paimon, Iceberg, Hudi. Now, only support Paimon. |
+| Option          | Type | Default | Description                                                                                                                                                                                                                 |
+|-----------------|------|---------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| datalake.format | Enum | (None)  | The datalake format used by of Fluss to be as lakehouse storage. Currently, supported formats are Paimon, Iceberg, and Lance. In the future, more kinds of data lake format will be supported, such as DeltaLake or Hudi.   |
 
 ## Kafka
 

website/docs/maintenance/configuration.md

@@ -14,7 +14,7 @@ Fluss organizes data into different storage layers based on its access patterns,
 Fluss ensures the recent data is stored in local for higher write/read performance and the historical data is stored in [remote storage](remote-storage.md) for lower cost.
 
 What's more, since the native format of Fluss's data is optimized for real-time write/read which is inevitable unfriendly to batch analytics, Fluss also introduces a [lakehouse storage](lakehouse-storage.md) which stores the data
-in the well-known open data lake format for better analytics performance. Currently, only Paimon is supported, but more kinds of data lake support are on the way. Keep eyes on us!
+in the well-known open data lake format for better analytics performance. Currently, supported formats are Paimon, Iceberg, and Lance. In the future, more kinds of data lake support are on the way. Keep eyes on us!
 
 The overall tiered storage architecture is shown in the following diagram:
 

website/docs/maintenance/tiered-storage/overview.md

@@ -5,10 +5,14 @@ sidebar_position: 1
 
 # Paimon
 
+## Introduction
+
 [Apache Paimon](https://paimon.apache.org/) innovatively combines a lake format with an LSM (Log-Structured Merge-tree) structure, bringing efficient updates into the lake architecture. 
 To integrate Fluss with Paimon, you must enable lakehouse storage and configure Paimon as the lakehouse storage. For more details, see [Enable Lakehouse Storage](maintenance/tiered-storage/lakehouse-storage.md#enable-lakehouse-storage).
 
-## Introduction
+## Configure Paimon as LakeHouse Storage
+
+For general guidance on configuring Paimon as the lakehouse storage, you can refer to [Lakehouse Storage](maintenance/tiered-storage/lakehouse-storage.md) documentation. When starting the tiering service, make sure to use Paimon-specific configurations as parameters.
 
 When a table is created or altered with the option `'table.datalake.enabled' = 'true'`, Fluss will automatically create a corresponding Paimon table with the same table path.
 The schema of the Paimon table matches that of the Fluss table, except for the addition of three system columns at the end: `__bucket`, `__offset`, and `__timestamp`.  

website/docs/streaming-lakehouse/integrate-data-lakes/paimon.md

@@ -44,4 +44,4 @@ Some powerful features it provided are:
 - **Analytical Streams**: The union reads help data streams to have the powerful analytics capabilities. This reduces complexity when developing streaming applications, simplifies debugging, and allows for immediate access to live data insights.
 - **Connect to Lakehouse Ecosystem**: Fluss keeps the table metadata in sync with data lake catalogs while compacting data into Lakehouse. This allows external engines like Spark, StarRocks, Flink, Trino to read the data directly by connecting to the data lake catalog.
 
-Currently, Fluss supports [Paimon](integrate-data-lakes/paimon.md) and [Lance](integrate-data-lakes/lance.md) as Lakehouse Storage, more kinds of data lake formats are on the roadmap.
+Currently, Fluss supports [Paimon](integrate-data-lakes/paimon.md), [Iceberg](integrate-data-lakes/iceberg.md), and [Lance](integrate-data-lakes/lance.md) as Lakehouse Storage, more kinds of data lake formats are on the roadmap.