[Improve][Doc] Unify the header format and fix some documents with abnormal formats (#9159)

Author: Hisoka-X

Diff for: docs/en/connector-v2/sink-common-options.md

@@ -22,7 +22,7 @@ When the job configuration `plugin_input` you must set the `plugin_output` param
 
 ## Task Example
 
-### Simple:
+### Simple
 
 > This is the process of passing a data source through two transforms and returning two different pipiles to different sinks
 

Diff for: docs/en/connector-v2/sink/Cloudberry.md

@@ -64,7 +64,7 @@ Key options include:
 
 ## Task Example
 
-### Simple:
+### Simple
 
 ```hocon
 env {
@@ -114,7 +114,7 @@ sink {
 }
 ```
 
-### Exactly-once:
+### Exactly-once
 
 ```hocon
 sink {

Diff for: docs/en/connector-v2/sink/Console.md

@@ -34,7 +34,7 @@ Used to send data to Console. Both support streaming and batch mode.
 
 ## Task Example
 
-### Simple:
+### Simple
 
 > This is a randomly generated data, written to the console, with a degree of parallelism of 1
 
@@ -63,7 +63,7 @@ sink {
 }
 ```
 
-### Multiple Sources Simple:
+### Multiple Sources Simple
 
 > This is a multiple source and you can specify a data source to write to the specified end
 

Diff for: docs/en/connector-v2/sink/DB2.md

@@ -88,7 +88,7 @@ semantics (using XA transaction guarantee).
 
 ## Task Example
 
-### Simple:
+### Simple
 
 > This example defines a SeaTunnel synchronization task that automatically generates data through FakeSource and sends it to JDBC Sink. FakeSource generates a total of 16 rows of data (row.num=16), with each row having two fields, name (string type) and age (int type). The final target table is test_table will also be 16 rows of data in the table. Before run this job, you need create database test and table test_table in your DB2. And if you have not yet installed and deployed SeaTunnel, you need to follow the instructions in [Install SeaTunnel](../../start-v2/locally/deployment.md) to install and deploy SeaTunnel. And then follow the instructions in [Quick Start With SeaTunnel Engine](../../start-v2/locally/quick-start-seatunnel-engine.md) to run this job.
 
@@ -153,7 +153,7 @@ sink {
 }
 ```
 
-### Exactly-once :
+### Exactly-once
 
 > For accurate write scene we guarantee accurate once
 

Diff for: docs/en/connector-v2/sink/Doris.md

@@ -176,7 +176,7 @@ Otherwise, if you enable the 2pc by the property `sink.enable-2pc=true`.The `sin
 
 ## Task Example
 
-### Simple:
+### Simple
 
 > The following example describes writing multiple data types to Doris, and users need to create corresponding tables downstream
 
@@ -234,7 +234,7 @@ sink {
 }
 ```
 
-### CDC(Change Data Capture) Event:
+### CDC(Change Data Capture) Event
 
 > This example defines a SeaTunnel synchronization task that automatically generates data through FakeSource and sends it to Doris Sink,FakeSource simulates CDC data with schema, score (int type),Doris needs to create a table sink named test.e2e_table_sink and a corresponding table for it.
 

Diff for: docs/en/connector-v2/sink/Feishu.md

@@ -52,7 +52,7 @@ Used to launch Feishu web hooks using data.
 
 ## Task Example
 
-### Simple:
+### Simple
 
 ```hocon
 Feishu {

Diff for: docs/en/connector-v2/sink/HdfsFile.md

@@ -86,7 +86,7 @@ Output data to hdfs file
 
 ## Task Example
 
-### Simple:
+### Simple
 
 > This example defines a SeaTunnel synchronization task that automatically generates data through FakeSource and sends it to Hdfs.
 

Diff for: docs/en/connector-v2/sink/Iceberg.md

@@ -83,7 +83,7 @@ libfb303-xxx.jar
 
 ## Task Example
 
-### Simple:
+### Simple
 
 ```hocon
 env {
@@ -128,7 +128,7 @@ sink {
 }
 ```
 
-### Hive Catalog:
+### Hive Catalog
 
 ```hocon
 sink {
@@ -154,7 +154,7 @@ sink {
 }
 ```
 
-### Hadoop catalog:
+### Hadoop catalog
 
 ```hocon
 sink {

Diff for: docs/en/connector-v2/sink/Kafka.md

@@ -99,7 +99,7 @@ This function by `MessageContentPartitioner` class implements `org.apache.kafka.
 
 ## Task Example
 
-### Simple:
+### Simple
 
 > This example defines a SeaTunnel synchronization task that automatically generates data through FakeSource and sends it to Kafka Sink. FakeSource generates a total of 16 rows of data (row.num=16), with each row having two fields, name (string type) and age (int type). The final target topic is test_topic will also be 16 rows of data in the topic. And if you have not yet installed and deployed SeaTunnel, you need to follow the instructions in [Install SeaTunnel](../../start-v2/locally/deployment.md) to install and deploy SeaTunnel. And then follow the instructions in [Quick Start With SeaTunnel Engine](../../start-v2/locally/quick-start-seatunnel-engine.md) to run this job.
 

Diff for: docs/en/connector-v2/sink/Kingbase.md

@@ -85,7 +85,7 @@ import ChangeLog from '../changelog/connector-jdbc.md';
 
 ## Task Example
 
-### Simple:
+### Simple
 
 > This example defines a SeaTunnel synchronization task that automatically generates data through FakeSource and sends
 > it to JDBC Sink. FakeSource generates a total of 16 rows of data (row.num=16), with each row having 12 fields. The final target table is test_table will also be 16 rows of data in the table.

Diff for: docs/en/connector-v2/sink/Kudu.md

@@ -57,7 +57,7 @@ import ChangeLog from '../changelog/connector-kudu.md';
 
 ## Task Example
 
-### Simple:
+### Simple
 
 > The following example refers to a FakeSource named "kudu" cdc write kudu table "kudu_sink_table"
 

Diff for: docs/en/connector-v2/sink/Mysql.md

@@ -99,7 +99,7 @@ semantics (using XA transaction guarantee).
 
 ## Task Example
 
-### Simple:
+### Simple
 
 > This example defines a SeaTunnel synchronization task that automatically generates data through FakeSource and sends it to JDBC Sink. FakeSource generates a total of 16 rows of data (row.num=16), with each row having two fields, name (string type) and age (int type). The final target table is test_table will also be 16 rows of data in the table. Before run this job, you need create database test and table test_table in your mysql. And if you have not yet installed and deployed SeaTunnel, you need to follow the instructions in [Install SeaTunnel](../../start-v2/locally/deployment.md) to install and deploy SeaTunnel. And then follow the instructions in [Quick Start With SeaTunnel Engine](../../start-v2/locally/quick-start-seatunnel-engine.md) to run this job.
 
@@ -164,7 +164,7 @@ sink {
 }
 ```
 
-### Exactly-once :
+### Exactly-once
 
 > For accurate write scene we guarantee accurate once
 

Diff for: docs/en/connector-v2/sink/Neo4j.md

@@ -118,6 +118,8 @@ sink {
 
 ## WriteBatchExample
 > The unwind keyword provided by cypher supports batch writing, and the default variable for a batch of data is batch. If you write a batch write statement, then you should declare cypher:unwind $batch as row to do someting
+ 
+
 ```
 sink {
   Neo4j {

Diff for: docs/en/connector-v2/sink/OceanBase.md

@@ -98,7 +98,7 @@ Write data through jdbc. Support Batch mode and Streaming mode, support concurre
 
 ## Task Example
 
-### Simple:
+### Simple
 
 > This example defines a SeaTunnel synchronization task that automatically generates data through FakeSource and sends it to JDBC Sink. FakeSource generates a total of 16 rows of data (row.num=16), with each row having two fields, name (string type) and age (int type). The final target table is test_table will also be 16 rows of data in the table. Before run this job, you need create database test and table test_table in your mysql. And if you have not yet installed and deployed SeaTunnel, you need to follow the instructions in [Install SeaTunnel](../../start-v2/locally/deployment.md) to install and deploy SeaTunnel. And then follow the instructions in [Quick Start With SeaTunnel Engine](../../start-v2/locally/quick-start-seatunnel-engine.md) to run this job.
 

Diff for: docs/en/connector-v2/sink/Oracle.md

@@ -98,7 +98,7 @@ semantics (using XA transaction guarantee).
 
 ## Task Example
 
-### Simple:
+### Simple
 
 > This example defines a SeaTunnel synchronization task that automatically generates data through FakeSource and sends it to JDBC Sink. FakeSource generates a total of 16 rows of data (row.num=16), with each row having two fields, name (string type) and age (int type). The final target table is test_table will also be 16 rows of data in the table. Before run this job, you need create database test and table test_table in your Oracle. And if you have not yet installed and deployed SeaTunnel, you need to follow the instructions in [Install SeaTunnel](../../start-v2/locally/deployment.md) to install and deploy SeaTunnel. And then follow the instructions in [Quick Start With SeaTunnel Engine](../../start-v2/locally/quick-start-seatunnel-engine.md) to run this job.
 
@@ -162,7 +162,7 @@ sink {
 }
 ```
 
-### Exactly-once :
+### Exactly-once
 
 > For accurate write scene we guarantee accurate once
 

Diff for: docs/en/connector-v2/sink/PostgreSql.md

@@ -142,7 +142,7 @@ When data_save_mode selects CUSTOM_PROCESSING, you should fill in the CUSTOM_SQL
 
 ## Task Example
 
-### Simple:
+### Simple
 
 > This example defines a SeaTunnel synchronization task that automatically generates data through FakeSource and sends it to JDBC Sink. FakeSource generates a total of 16 rows of data (row.num=16), with each row having two fields, name (string type) and age (int type). The final target table is test_table will also be 16 rows of data in the table. Before run this job, you need create database test and table test_table in your PostgreSQL. And if you have not yet installed and deployed SeaTunnel, you need to follow the instructions in [Install SeaTunnel](../../start-v2/locally/deployment.md) to install and deploy SeaTunnel. And then follow the instructions in [Quick Start With SeaTunnel Engine](../../start-v2/locally/quick-start-seatunnel-engine.md) to run this job.
 
@@ -208,7 +208,7 @@ sink {
 }
 ```
 
-### Exactly-once :
+### Exactly-once
 
 > For accurate write scene we guarantee accurate once
 

Diff for: docs/en/connector-v2/sink/Pulsar.md

@@ -132,7 +132,7 @@ Source plugin common parameters, please refer to [Source Common Options](../sink
 
 ## Task Example
 
-### Simple:
+### Simple
 
 > This example defines a SeaTunnel synchronization task that automatically generates data through FakeSource and sends it to Pulsar Sink. FakeSource generates a total of 16 rows of data (row.num=16), with each row having two fields, name (string type) and age (int type). The final target topic is test_topic will also be 16 rows of data in the topic. And if you have not yet installed and deployed SeaTunnel, you need to follow the instructions in [Install SeaTunnel](../../start-v2/locally/deployment.md) to install and deploy SeaTunnel. And then follow the instructions in [Quick Start With SeaTunnel Engine](../../start-v2/locally/quick-start-seatunnel-engine.md) to run this job.
 

Diff for: docs/en/connector-v2/sink/Redshift.md

@@ -60,7 +60,7 @@ semantics (using XA transaction guarantee).
 
 ## Task Example
 
-### Simple:
+### Simple
 
 ```
 sink {

Diff for: docs/en/connector-v2/sink/S3File.md

@@ -314,7 +314,7 @@ The encoding of the file to write. This param will be parsed by `Charset.forName
 
 ## Example
 
-### Simple:
+### Simple
 
 > This example defines a SeaTunnel synchronization task that automatically generates data through FakeSource and sends it to S3File Sink. FakeSource generates a total of 16 rows of data (row.num=16), with each row having two fields, name (string type) and age (int type). The final target s3 dir will also create a file and all of the data in write in it.
 > Before run this job, you need create s3 path: /seatunnel/text. And if you have not yet installed and deployed SeaTunnel, you need to follow the instructions in [Install SeaTunnel](../../start-v2/locally/deployment.md) to install and deploy SeaTunnel. And then follow the instructions in [Quick Start With SeaTunnel Engine](../../start-v2/locally/quick-start-seatunnel-engine.md) to run this job.

Diff for: docs/en/connector-v2/sink/SelectDB-Cloud.md

@@ -79,7 +79,7 @@ The supported formats include CSV and JSON
 
 ## Task Example
 
-### Simple:
+### Simple
 
 > The following example describes writing multiple data types to SelectDBCloud, and users need to create corresponding tables downstream
 

Diff for: docs/en/connector-v2/sink/Slack.md

@@ -36,7 +36,7 @@ All data types are mapped to string.
 
 ## Task Example
 
-### Simple:
+### Simple
 
 ```hocon
 sink {

Diff for: docs/en/connector-v2/sink/Snowflake.md

@@ -77,7 +77,7 @@ Write data through jdbc. Support Batch mode and Streaming mode, support concurre
 >
   ## Task Example
 
-### simple:
+### simple
 
 > This example defines a SeaTunnel synchronization task that automatically generates data through FakeSource and sends it to JDBC Sink. FakeSource generates a total of 16 rows of data (row.num=16), with each row having two fields, name (string type) and age (int type). The final target table is test_table will also be 16 rows of data in the table. Before run this job, you need create database test and table test_table in your snowflake database. And if you have not yet installed and deployed SeaTunnel, you need to follow the instructions in [Install SeaTunnel](../../start-v2/locally/deployment.md) to install and deploy SeaTunnel. And then follow the instructions in [Quick Start With SeaTunnel Engine](../../start-v2/locally/quick-start-seatunnel-engine.md) to run this job.
 

Diff for: docs/en/connector-v2/sink/SqlServer.md

@@ -96,7 +96,7 @@ semantics (using XA transaction guarantee).
 
 ## Task Example
 
-### simple:
+### simple
 
 > This is one that reads Sqlserver data and inserts it directly into another table
 

Diff for: docs/en/connector-v2/sink/StarRocks.md

@@ -165,7 +165,7 @@ The supported formats include CSV and JSON
 
 ## Task Example
 
-### Simple:
+### Simple
 
 > The following example describes writing multiple data types to StarRocks, and users need to create corresponding tables downstream
 

Diff for: docs/en/connector-v2/sink/Vertica.md

@@ -96,7 +96,7 @@ semantics (using XA transaction guarantee).
 
 ## Task Example
 
-### Simple:
+### Simple
 
 > This example defines a SeaTunnel synchronization task that automatically generates data through FakeSource and sends it to JDBC Sink. FakeSource generates a total of 16 rows of data (row.num=16), with each row having two fields, name (string type) and age (int type). The final target table is test_table will also be 16 rows of data in the table. Before run this job, you need create database test and table test_table in your vertical. And if you have not yet installed and deployed SeaTunnel, you need to follow the instructions in [Install SeaTunnel](../../start-v2/locally/deployment.md) to install and deploy SeaTunnel. And then follow the instructions in [Quick Start With SeaTunnel Engine](../../start-v2/locally/quick-start-seatunnel-engine.md) to run this job.
 
@@ -161,7 +161,7 @@ sink {
 }
 ```
 
-### Exactly-once :
+### Exactly-once
 
 > For accurate write scene we guarantee accurate once
 

Diff for: docs/en/connector-v2/source-common-options.md

@@ -23,7 +23,7 @@ When the job configuration `plugin_output` you must set the `plugin_input` param
 
 ## Task Example
 
-### Simple:
+### Simple
 
 > This registers a stream or batch data source and returns the table name `fake_table` at registration
 

Diff for: docs/en/connector-v2/source/Cloudberry.md

@@ -67,7 +67,7 @@ Cloudberry supports parallel reading following the same rules as PostgreSQL conn
 
 ## Task Example
 
-### Simple:
+### Simple
 
 ```hocon
 env {
@@ -90,7 +90,7 @@ sink {
 }
 ```
 
-### Parallel reading with table_path:
+### Parallel reading with table_path
 
 ```hocon
 env {
@@ -114,7 +114,7 @@ sink {
 }
 ```
 
-### Multiple table read:
+### Multiple table read
 
 ```hocon
 env {

Diff for: docs/en/connector-v2/source/DB2.md

@@ -88,7 +88,7 @@ Read external data source data through JDBC.
 
 ## Task Example
 
-### Simple:
+### Simple
 
 > This example queries type_bin 'table' 16 data in your test "database" in single parallel and queries all of its fields. You can also specify which fields to query for final output to the console.
 
@@ -119,7 +119,7 @@ sink {
 }
 ```
 
-### Parallel:
+### Parallel
 
 > Read your query table in parallel with the shard field you configured and the shard data  You can do this if you want to read the whole table
 
@@ -141,7 +141,7 @@ source {
 }
 ```
 
-### Parallel Boundary:
+### Parallel Boundary
 
 > It is more efficient to specify the data within the upper and lower bounds of the query It is more efficient to read your data source according to the upper and lower boundaries you configured