lakehq · linhr · Mar 2, 2026 · Mar 2, 2026 · Mar 2, 2026
diff --git a/docs/guide/formats/index.md b/docs/guide/formats/index.md
diff --git a/docs/guide/integrations/jdbc.md b/docs/guide/integrations/jdbc.md
diff --git a/docs/guide/formats/delta.md → docs/guide/sources/delta/examples.md b/docs/guide/formats/delta.md → docs/guide/sources/delta/examples.md
@@ -1,18 +1,13 @@
 ---
-title: Delta Lake
+title: Examples
 rank: 1
 ---
 
-# Delta Lake
+# Examples
 
-You can use the `delta` format in Sail to work with [Delta Lake](https://delta.io/).
-You can use the Spark DataFrame API or Spark SQL to read and write Delta tables.
+<!--@include: ../../_common/spark-session.md-->
 
-## Examples
-
-<!--@include: ../_common/spark-session.md-->
-
-### Basic Usage
+## Basic Usage
 
 ::: code-group
 
@@ -44,7 +39,7 @@ SELECT * FROM users;
 
 :::
 
-### Data Partitioning
+## Data Partitioning
 
 You can work with partitioned Delta tables using the Spark DataFrame API.
 Partitioned Delta tables organize data into directories based on the values of one or more columns.
@@ -78,7 +73,7 @@ SELECT * FROM metrics WHERE year > 2024;
 
 :::
 
-### Schema Evolution
+## Schema Evolution
 
 Delta Lake handles schema evolution gracefully.
 By default, if you try to write data with a different schema than the one of the existing Delta table, an error will occur.
@@ -96,7 +91,7 @@ But this works only if you set the write mode to `overwrite`.
 df.write.format("delta").mode("overwrite").option("overwriteSchema", "true").save(path)
 ```
 
-### Time Travel
+## Time Travel
 
 You can use the time travel feature to query historical versions of a Delta table.
 
@@ -107,7 +102,7 @@ df = spark.read.format("delta").option("timestampAsOf", "2025-01-02T03:04:05.678
 
 Time travel is not available for Spark SQL in Sail yet, but we plan to support it soon.
 
-### Column Mapping
+## Column Mapping
 
 You can write Delta tables with column mapping enabled. The supported column mapping modes are `name` and `id`. You must write to a new Delta table to enable column mapping.
 
@@ -118,7 +113,7 @@ df.write.format("delta").option("columnMappingMode", "id").save(path)
 
 Existing Delta tables with column mapping can be read as usual.
 
-### More Features
+## More Features
 
 We will continue adding more examples for advanced Delta Lake features as they become available in Sail.
 In the meantime, feel free to reach out to us on [Slack](https://lakesail.com/slack) or [GitHub Discussions](https://github.com/lakehq/sail/discussions) if you have questions!
diff --git a/docs/guide/sources/delta/features.md b/docs/guide/sources/delta/features.md
@@ -0,0 +1,56 @@
+---
+title: Supported Features
+rank: 2
+---
+
+# Supported Features
+
+## Core Table Operations
+
+| Feature                                     | Supported          |
+| ------------------------------------------- | ------------------ |
+| Read                                        | :white_check_mark: |
+| Write (append)                              | :white_check_mark: |
+| Write (overwrite)                           | :white_check_mark: |
+| Data skipping (partition pruning)           | :white_check_mark: |
+| Data skipping (pruning via file statistics) | :white_check_mark: |
+| Schema validation                           | :white_check_mark: |
+| Schema evolution                            | :white_check_mark: |
+| Time travel (by version)                    | :white_check_mark: |
+| Time travel (by timestamp)                  | :white_check_mark: |
+
+Both non-partitioned and partitioned tables are supported for reading and writing.
+
+## DML Operations
+
+| Feature                  | Supported          |
+| ------------------------ | ------------------ |
+| `DELETE` (copy-on-write) | :white_check_mark: |
+| `MERGE` (copy-on-write)  | :white_check_mark: |
+| `DELETE` (merge-on-read) | :construction:     |
+| `MERGE` (merge-on-read)  | :construction:     |
+| `UPDATE`                 | :construction:     |
+
+The "merge-on-read" mode refers to updating the table with deletion vectors. This reduces the amount of data that needs to be rewritten during DML operations, but incurs additional read overhead when querying the table.
+
+## Table Maintenance Operations
+
+| Feature    | Supported      |
+| ---------- | -------------- |
+| `VACUUM`   | :construction: |
+| `OPTIMIZE` | :construction: |
+| `RESTORE`  | :construction: |
+
+## Protocol Internals
+
+| Feature                          | Supported          |
+| -------------------------------- | ------------------ |
+| Checkpointing                    | :white_check_mark: |
+| Log clean-up                     | :white_check_mark: |
+| Column mapping                   | :white_check_mark: |
+| Deletion vectors                 | :construction:     |
+| Constraints                      | :construction:     |
+| Identity columns                 | :construction:     |
+| Generated columns                | :construction:     |
+| Transaction (conflict detection) | :construction:     |
+| Change data feed                 | :construction:     |
diff --git a/docs/guide/sources/delta/index.data.ts b/docs/guide/sources/delta/index.data.ts
@@ -0,0 +1,5 @@
+import { createContentLoader } from "vitepress";
+
+export default createContentLoader([
+  "/guide/sources/delta/!(index|_*/**|**/_*/**).md",
+]);
diff --git a/docs/guide/sources/delta/index.md b/docs/guide/sources/delta/index.md
@@ -0,0 +1,18 @@
+---
+title: Delta Lake
+rank: 1
+---
+
+# Delta Lake
+
+You can use the `delta` format in Sail to work with [Delta Lake](https://delta.io/).
+You can use the Spark DataFrame API or Spark SQL to read and write Delta tables.
+
+## Topics
+
+<PageList :data="data" :prefix="['guide', 'sources', 'delta']" />
+
+<script setup>
+import PageList from "@theme/components/PageList.vue";
+import { data } from "./index.data.ts";
+</script>
diff --git a/docs/guide/formats/iceberg.md → docs/guide/sources/iceberg/examples.md b/docs/guide/formats/iceberg.md → docs/guide/sources/iceberg/examples.md
@@ -1,18 +1,13 @@
 ---
-title: Iceberg
-rank: 2
+title: Examples
+rank: 1
 ---
 
-# Iceberg
+# Examples
 
-You can use the `iceberg` format in Sail to work with [Apache Iceberg](https://iceberg.apache.org/).
-You can use the Spark DataFrame API or Spark SQL to read and write Iceberg tables.
+<!--@include: ../../_common/spark-session.md-->
 
-## Examples
-
-<!--@include: ../_common/spark-session.md-->
-
-### Basic Usage
+## Basic Usage
 
 ::: code-group
 
@@ -44,7 +39,7 @@ SELECT * FROM users;
 
 :::
 
-### Data Partitioning
+## Data Partitioning
 
 You can work with partitioned Iceberg tables using the Spark DataFrame API.
 Partitioned Iceberg tables organize data into directories based on the values of one or more columns.
@@ -78,7 +73,7 @@ SELECT * FROM metrics WHERE year > 2024;
 
 :::
 
-### Time Travel
+## Time Travel
 
 You can use the time travel feature to query tags, branches, or historical versions of an Iceberg table.
 
@@ -90,7 +85,7 @@ df = spark.read.format("iceberg").option("branch", "main").load(path)
 
 Time travel is not available for Spark SQL in Sail yet, but we plan to support it soon.
 
-### More Features
+## More Features
 
 We will continue adding more examples for advanced Iceberg features as they become available in Sail.
 In the meantime, feel free to reach out to us on [Slack](https://lakesail.com/slack) or [GitHub Discussions](https://github.com/lakehq/sail/discussions) if you have questions!