update MD by dispatch event pingcap/docs feature/preview-cloud-lake

Author: github-actions

markdown-pages/en/tidb/release-8.5/TOC-tidb-cloud-lake.md

@@ -780,8 +780,8 @@
       - [ARRAY_AGG](/tidb-cloud-lake/sql/array-agg.md)
       - [AVG](/tidb-cloud-lake/sql/avg.md)
       - [AVG_IF](/tidb-cloud-lake/sql/avg-if.md)
-      - [bool_and](/tidb-cloud-lake/sql/bool.md)
-      - [bool_or](/tidb-cloud-lake/sql/bool-sql.md)
+      - [bool_and](/tidb-cloud-lake/sql/bool-and.md)
+      - [bool_or](/tidb-cloud-lake/sql/bool-or.md)
       - [COUNT](/tidb-cloud-lake/sql/count.md)
       - [COUNT_DISTINCT](/tidb-cloud-lake/sql/count-distinct.md)
       - [COUNT_IF](/tidb-cloud-lake/sql/count-if.md)

markdown-pages/en/tidb/release-8.5/tidb-cloud-lake/_index.md

@@ -5,6 +5,8 @@ hide_commit: true
 summary: TiDB Cloud Lake is cloud-native data warehouse service focused on analytics workloads that scales elastically and supports ANSI SQL and multi-modal data operations.
 ---
 
+# TiDB Cloud Lake Documentation
+
 <LearningPathContainer platform="tidb-cloud" title="TiDB Cloud Lake" subTitle="TiDB Cloud Lake is cloud-native data warehouse service focused on analytics workloads that scales elastically and supports ANSI SQL and multi-modal data operations.">
 
 </LearningPathContainer>

markdown-pages/en/tidb/release-8.5/tidb-cloud-lake/guides/access-control.md

@@ -2,6 +2,9 @@
 title: Access Control
 summary: Learn about access control in TiDB Cloud Lake, which uses both Role-Based Access Control (RBAC) and Discretionary Access Control (DAC) to manage permissions for data objects like databases, tables, and views.
 ---
+
+# Access Control
+
 Databend incorporates both [Role-Based Access Control (RBAC)](https://en.wikipedia.org/wiki/Role-based_access_control) and [Discretionary Access Control (DAC)](https://en.wikipedia.org/wiki/Discretionary_access_control) models for its access control functionality. When a user accesses a data object in Databend, they must be granted appropriate privileges or roles, or they need to have ownership of the data object. A data object can refer to various elements, such as a database, table, view, stage, or UDF.
 
 ![Access control](https://docs-download.pingcap.com/media/images/docs/tidb-cloud-lake/access-control-1.png)
@@ -16,4 +19,4 @@ This guide describes the related concepts and provides instructions on how to ma
 
 - [Privileges](/tidb-cloud-lake/guides/privileges.md)
 - [Roles](/tidb-cloud-lake/guides/roles.md)
-- [Ownership](/tidb-cloud-lake/guides/ownership.md)
+- [Ownership](/tidb-cloud-lake/guides/ownership.md)

markdown-pages/en/tidb/release-8.5/tidb-cloud-lake/guides/aggregating-index.md

@@ -2,7 +2,8 @@
 title: Aggregating Index
 summary: Aggregating indexes dramatically accelerate analytical queries by precomputing and storing aggregation results, eliminating the need to scan entire tables for common analytics operations.
 ---
-# Aggregating Index: Precomputed Results for Instant Analytics
+
+# Aggregating Index
 
 Aggregating indexes dramatically accelerate analytical queries by precomputing and storing aggregation results, eliminating the need to scan entire tables for common analytics operations.
 
@@ -34,9 +35,9 @@ Analytical queries on large datasets face significant performance challenges:
 CREATE TABLE sales(region VARCHAR, product VARCHAR, revenue DECIMAL, quantity INT);
 
 -- Create aggregating index for common analytics
-CREATE AGGREGATING INDEX sales_summary AS 
-SELECT region, SUM(revenue), COUNT(*), AVG(quantity) 
-FROM sales 
+CREATE AGGREGATING INDEX sales_summary AS
+SELECT region, SUM(revenue), COUNT(*), AVG(quantity)
+FROM sales
 GROUP BY region;
 
 -- Refresh the index (manual mode)
@@ -67,11 +68,11 @@ EXPLAIN SELECT region, SUM(revenue) FROM sales GROUP BY region;
 
 ```sql
 -- Automatic refresh (updates with every data change)
-CREATE AGGREGATING INDEX auto_summary AS 
+CREATE AGGREGATING INDEX auto_summary AS
 SELECT region, SUM(revenue) FROM sales GROUP BY region SYNC;
 
 -- Manual refresh (update on demand)
-CREATE AGGREGATING INDEX manual_summary AS 
+CREATE AGGREGATING INDEX manual_summary AS
 SELECT region, SUM(revenue) FROM sales GROUP BY region;
 
 REFRESH AGGREGATING INDEX manual_summary;

markdown-pages/en/tidb/release-8.5/tidb-cloud-lake/guides/ai-ml-integration.md

@@ -2,6 +2,7 @@
 title: AI & ML Integration
 summary: Databend enables powerful AI and ML capabilities through two complementary approaches build custom AI functions with your own infrastructure, or create conversational data experiences using natural language.
 ---
+
 # AI & ML Integration
 
 Databend enables powerful AI and ML capabilities through two complementary approaches: build custom AI functions with your own infrastructure, or create conversational data experiences using natural language.

markdown-pages/en/tidb/release-8.5/tidb-cloud-lake/guides/ai-powered-features.md

@@ -3,6 +3,8 @@ title: AI-Powered Features
 summary: With the inclusion of AI-powered features, TiDB Cloud Lake allows you to engage in natural language conversations to receive help, assistance, and solutions.
 ---
 
+# AI-Powered Features
+
 With the inclusion of AI-powered features, Databend Cloud allows you to engage in natural language conversations to receive help, assistance, and solutions. These AI-powered features are enabled by default, but you can disable them if desired by navigating to **Manage** > **Settings**.
 
 ### AI Chat for Assistance

markdown-pages/en/tidb/release-8.5/tidb-cloud-lake/guides/audit-trail.md

@@ -2,6 +2,9 @@
 title: Audit Trail
 summary: Databend system history tables automatically capture detailed records of database activities, providing a complete audit trail for compliance and security monitoring.
 ---
+
+# Audit Trail
+
 Databend system history tables automatically capture detailed records of database activities, providing a complete audit trail for compliance and security monitoring.
 
 Allows the auditing of the user:
@@ -29,8 +32,8 @@ Track authentication failures to identify potential security threats and unautho
 
 ```sql
 -- Check for failed login attempts (security audit)
-SELECT event_time, user_name, client_ip, error_message 
-FROM system_history.login_history 
+SELECT event_time, user_name, client_ip, error_message
+FROM system_history.login_history
 WHERE event_type = 'LoginFailed'
 ORDER BY event_time DESC;
 ```
@@ -52,7 +55,7 @@ Monitor DDL operations for compliance and change management requirements.
 ```sql
 -- Audit DDL operations (compliance tracking)
 SELECT query_id, query_start, user_name, object_modified_by_ddl
-FROM system_history.access_history 
+FROM system_history.access_history
 WHERE object_modified_by_ddl != '[]'
 ORDER BY query_start DESC;
 ```
@@ -72,7 +75,7 @@ Track who accessed what data and when for compliance and data governance.
 ```sql
 -- Track data access for compliance
 SELECT query_id, query_start, user_name, base_objects_accessed
-FROM system_history.access_history 
+FROM system_history.access_history
 WHERE base_objects_accessed != '[]'
 ORDER BY query_start DESC;
 ```
@@ -86,7 +89,7 @@ Maintain comprehensive records of all SQL operations with user and timing inform
 ```sql
 -- Complete query audit with user and timing information
 SELECT query_id, sql_user, query_text, query_start_time, query_duration_ms, client_address
-FROM system_history.query_history 
+FROM system_history.query_history
 WHERE event_date >= TODAY() - INTERVAL 7 DAY
 ORDER BY query_start_time DESC;
 ```

markdown-pages/en/tidb/release-8.5/tidb-cloud-lake/guides/authenticate-with-aws-iam-role.md

@@ -2,6 +2,7 @@
 title: Authenticate with AWS IAM Role
 summary: Cloud-native identity delegation (AWS IAM Role, Azure Managed Identity, Google Service Account federation, etc.) lets Databend Cloud obtain short-lived credentials to your object storage without ever handling raw access keys. That keeps data plane access inside your cloud provider's control plane while you retain ownership of every permission.
 ---
+
 # Authenticate with AWS IAM Role
 
 Cloud-native identity delegation (AWS IAM Role, Azure Managed Identity, Google Service Account federation, etc.) lets Databend Cloud obtain short-lived credentials to your object storage without ever handling raw access keys. That keeps data plane access inside your cloud provider's control plane while you retain ownership of every permission.

markdown-pages/en/tidb/release-8.5/tidb-cloud-lake/guides/automate-data-loading-with-tasks.md

@@ -2,6 +2,9 @@
 title: Automating Data Loading with Tasks
 summary: Tasks wrap SQL so Databend can run it for you on a schedule or when a condition is met. Keep the following knobs in mind when you define one with CREATE TASK.
 ---
+
+# Automating Data Loading with Tasks
+
 Tasks wrap SQL so Databend can run it for you on a schedule or when a condition is met. Keep the following knobs in mind when you define one with [CREATE TASK](/tidb-cloud-lake/sql/create-task.md):
 
 ![alt text](https://docs-download.pingcap.com/media/images/docs/tidb-cloud-lake/task.png)
@@ -211,4 +214,3 @@ FROM task_history('task_stream_merge', 5);
 ```
 
 The task fires only when `STREAM_STATUS('<database>.<stream_name>')` returns `TRUE`. Always prefix the stream with its database (for example `task_demo.sensor_events_stream`) so the task can resolve it regardless of the current schema, and use your own warehouse name in every `CREATE/ALTER TASK`.
-

markdown-pages/en/tidb/release-8.5/tidb-cloud-lake/guides/cluster-key-performance.md

@@ -2,7 +2,8 @@
 title: Cluster Key
 summary: Cluster keys provide automatic data organization to dramatically improve query performance on large tables. Databend seamlessly and continually manages all clustering operations in the background - you simply define the cluster key and Databend handles the rest.
 ---
-# Cluster Key: Automatic Data Organization for Query Acceleration
+
+# Cluster Key
 
 Cluster keys provide automatic data organization to dramatically improve query performance on large tables. Databend seamlessly and continually manages all clustering operations in the background - you simply define the cluster key and Databend handles the rest.