Update connector-snowflake.md

Made changes suggested by Acrolinx to increase the overall score to at least 80

Author: PMEds28

articles/data-factory/connector-snowflake.md

@@ -272,7 +272,7 @@ If your sink data store and format meet the criteria described in this section,
 
 #### Staged copy from Snowflake
 
-When your sink data store or format is not natively compatible with the Snowflake COPY command, as mentioned in the last section, enable the built-in staged copy using an interim Azure Blob storage instance. The staged copy feature also provides you better throughput. The service exports data from Snowflake into staging storage, then copies the data to sink, and finally cleans up your temporary data from the staging storage. See [Staged copy](copy-activity-performance-features.md#staged-copy) for details about copying data by using staging.
+When your sink data store or format is not natively compatible with the Snowflake COPY command, as mentioned in the last section, enable the built-in staged copy using an interim Azure Blob storage instance. The staged copy feature also provides you with better throughput. The service exports data from Snowflake into staging storage, then copies the data to sink, and finally cleans up your temporary data from the staging storage. See [Staged copy](copy-activity-performance-features.md#staged-copy) for details about copying data by using staging.
 
 To use this feature, create an [Azure Blob storage linked service](connector-azure-blob-storage.md#linked-service-properties) that refers to the Azure storage account as the interim staging. Then specify the `enableStaging` and `stagingSettings` properties in the Copy activity.
 
@@ -418,7 +418,7 @@ If your source data store and format meet the criteria described in this section
 
 #### Staged copy to Snowflake
 
-When your source data store or format is not natively compatible with the Snowflake COPY command, as mentioned in the last section, enable the built-in staged copy using an interim Azure Blob storage instance. The staged copy feature also provides you better throughput. The service automatically converts the data to meet the data format requirements of Snowflake. It then invokes the COPY command to load data into Snowflake. Finally, it cleans up your temporary data from the blob storage. See [Staged copy](copy-activity-performance-features.md#staged-copy) for details about copying data using staging.
+When your source data store or format is not natively compatible with the Snowflake COPY command, as mentioned in the last section, enable the built-in staged copy using an interim Azure Blob storage instance. The staged copy feature also provides you with better throughput. The service automatically converts the data to meet the data format requirements of Snowflake. It then invokes the COPY command to load data into Snowflake. Finally, it cleans up your temporary data from the blob storage. See [Staged copy](copy-activity-performance-features.md#staged-copy) for details about copying data using staging.
 
 To use this feature, create an [Azure Blob storage linked service](connector-azure-blob-storage.md#linked-service-properties) that refers to the Azure storage account as the interim staging. Then specify the `enableStaging` and `stagingSettings` properties in the Copy activity.
 
@@ -508,7 +508,7 @@ source(allowSchemaDrift: true,
 ```
 ### Native Change Tracking
 
-Azure Data Factory now supports a native feature in Snowflake known as change tracking, which involves tracking changes in the form of logs. This feature of snowflake allows us to track the changes in the data over time making it useful for incremental data loading and auditing purpose. To utilize this feature, when you enable Change data capture and select the Snowflake Change Tracking, we create a Stream object for the source table, that enables change tracking on source snowflake table. Subsequently, we use the CHANGES clause in our query to fetch only the new or updated data from source table. Also, it is recommended to schedule pipeline such that changes are consumed within interval of [data retention time](https://docs.snowflake.com/en/sql-reference/parameters#label-data-retention-time-in-days) set for snowflake source table else user might see inconsistent behavior in captured changes.
+Azure Data Factory now supports a native feature in Snowflake known as change tracking, which involves tracking changes in the form of logs. This feature of snowflake allows us to track the changes in the data over time making it useful for incremental data loading and auditing purpose. To utilize this feature, when you enable Change data capture and select the Snowflake Change Tracking, we create a Stream object for the source table that enables change tracking on source snowflake table. Subsequently, we use the CHANGES clause in our query to fetch only the new or updated data from source table. Also, it is recommended to schedule pipeline such that changes are consumed within interval of [data retention time](https://docs.snowflake.com/en/sql-reference/parameters#label-data-retention-time-in-days) set for snowflake source table else user might see inconsistent behavior in captured changes.
 
 
 ### Sink transformation