*: update wording for accuracy

clustered-indexes.md

@@ -7,7 +7,7 @@ summary: Learn the concept, user scenarios, usages, limitations, and compatibili
 
 TiDB supports the clustered index feature since v5.0. This feature controls how data is stored in tables containing primary keys. It provides TiDB the ability to organize tables in a way that can improve the performance of certain queries.
 
-The term _clustered_ in this context refers to the _organization of how data is stored_ and not _a group of database servers working together_. Some database management systems refer to clustered indexes as _index-organized tables_ (IOT).
+The term _clustered_ in this context refers to the _organization of how data is stored_ and not _a group of database servers working together_. Some database management systems refer to clustered index tables as _index-organized tables_ (IOT).
 
 Currently, tables containing primary keys in TiDB are divided into the following two categories:
 

develop/dev-guide-create-table.md

@@ -135,7 +135,7 @@ CREATE TABLE `bookshop`.`users` (
 
 TiDB supports the [clustered index](/clustered-indexes.md) feature since v5.0. This feature controls how data is stored in tables containing primary keys. It provides TiDB the ability to organize tables in a way that can improve the performance of certain queries.
 
-The term clustered in this context refers to the organization of how data is stored and not a group of database servers working together. Some database management systems refer to clustered indexes as index-organized tables (IOT).
+The term clustered in this context refers to the organization of how data is stored and not a group of database servers working together. Some database management systems refer to clustered index tables as index-organized tables (IOT).
 
 Currently, tables **_containing primary_** keys in TiDB are divided into the following two categories:
 

releases/release-5.0.0.md

@@ -177,7 +177,7 @@ Currently, the main features that the MPP mode does not support are as follows (
 
 When you are designing table structures or analyzing database behaviors, it is recommended to use the clustered index feature if you find that some columns with primary keys are often grouped and sorted, queries on these columns often return a certain range of data or a small amount of data with different values, and the corresponding data does not cause read or write hotspot issues.
 
-Clustered indexes, also known as _index-organized tables_ in some database management systems, is a storage structure associated with the data of a table. When creating a clustered index, you can specify one or more columns from the table as the keys for the index. TiDB stores these keys in a specific structure, which allows TiDB to quickly and efficiently find the rows associated with the keys, thus improves the performance of querying and writing data.
+Clustered indexes is a storage structure associated with the data of a table. Some database management systems refer to clustered index tables as _index-organized tables_. When creating a clustered index, you can specify one or more columns from the table as the keys for the index. TiDB stores these keys in a specific structure, which allows TiDB to quickly and efficiently find the rows associated with the keys, thus improves the performance of querying and writing data.
 
 When the clustered index feature is enabled, the TiDB performance improves significantly (for example in the TPC-C tpmC test, the performance of TiDB, with clustered index enabled, improves by 39%) in the following cases:
 

ticdc/ticdc-canal-json.md

@@ -42,7 +42,7 @@ cdc cli changefeed create --server=http://127.0.0.1:8300 --changefeed-id="kafka-
 
 ## Definitions of message formats
 
-This section describes the formats of DDL Event, DML Event and WATERMARK Event, and how the data is resolved on the consumer side.
+This section describes the formats of DDL Event, DML Event and WATERMARK Event, and how the data is parsed on the consumer side.
 
 ### DDL Event
 
@@ -167,9 +167,9 @@ The following is an example of the WATERMARK Event.
 }
 ```
 
-### Data resolution on the consumer side
+### Data parsing on the consumer side
 
-As you can see from the example above, Canal-JSON has a uniform data format, with different field filling rules for different Event types. You can use a uniform method to resolve this JSON format data, and then determine the Event type by checking the field values.
+As you can see from the example above, Canal-JSON has a unified data format, with different field filling rules for different Event types. Consumers can parse data in this JSON format using a unified method, and then determine the Event type by checking the field values:
 
 * When `isDdl` is `true`, the message contains a DDL Event.
 * When `isDdl` is `false`, you need to further check the `type` field. If `type` is `TIDB_WATERMARK`, it is a WATERMARK Event; otherwise, it is a DML Event.