acrolinx

v-dirichards · web-flow · commit da783f6c45f7 · 2024-03-08T15:21:25.000-06:00
diff --git a/articles/data-factory/connector-snowflake.md b/articles/data-factory/connector-snowflake.md
@@ -94,7 +94,7 @@ These generic properties are supported for the Snowflake linked service:
 | :--------------- | :----------------------------------------------------------- | :------- |
 | type             | The type property must be set to **SnowflakeV2**.              | Yes      |
 | accountIdentifier | The name of the account along with its organization. For example, myorg-account123.   | Yes |
-| database | The default database uesd for the session after connecting. | Yes |
+| database | The default database used for the session after connecting. | Yes |
 | warehouse | The default virtual warehouse used for the session after connecting. |Yes|
 | authenticationType | Type of authentication used to connect to the Snowflake service. Allowed values are: **Basic** (Default) and  **KeyPair**. Refer to corresponding sections below on more properties and examples respectively.  | No |
 | role | The default security role used for the session after connecting. | No |
@@ -181,7 +181,7 @@ In addition to the generic properties that are described in the preceding sectio
 | :--------------- | :----------------------------------------------------------- | :------- |
 | user | Login name for the Snowflake user. | Yes |
 | privateKey | The private key used for the key pair authentication. <br/><br/>To ensure the private key is valid when sent to Azure Data Factory, and considering that the privateKey file includes newline characters (\n), it's essential to correctly format the privateKey content in its string literal form. This process involves adding \n explicitly to each newline.   | Yes |
-| privateKeyPassphrase | The passphrase used for decrypting the private key, if it is encrypted.  | No |
+| privateKeyPassphrase | The passphrase used for decrypting the private key, if it's encrypted.  | No |
 
 **Example:**
 
@@ -548,7 +548,7 @@ The below table lists the properties supported by Snowflake source. You can edit
 | Enable incremental extract (Preview) | Use this option to tell ADF to only process rows that have changed since the last time that the pipeline executed. | No | Boolean | enableCdc |
 | Incremental Column | When using the incremental extract feature, you must choose the date/time/numeric column that you wish to use as the watermark in your source table. | No | String | waterMarkColumn |
 | Enable Snowflake Change Tracking (Preview) | This option enables ADF to leverage Snowflake change data capture technology to process only the delta data since the previous pipeline execution. This option automatically loads the delta data with row insert, update and deletion operations without requiring any incremental column. | No | Boolean | enableNativeCdc |
-| Net Changes | When using snowflake change tracking, you can use this option to get deduped changed rows or exhaustive changes. Deduped changed rows will show only the latest versions of the rows that have changed since a given point in time, while exhaustive changes will show you all the versions of each row that has changed, including the ones that were deleted or updated. For example, if you update a row, you will see a delete version and an insert version in exhaustive changes, but only the insert version in deduped changed rows. Depending on your use case, you can choose the option that suits your needs. The default option is false, which means exhaustive changes. | No | Boolean | netChanges |
+| Net Changes | When using snowflake change tracking, you can use this option to get deduped changed rows or exhaustive changes. Deduped changed rows will show only the latest versions of the rows that have changed since a given point in time, while exhaustive changes will show you all the versions of each row that has changed, including the ones that were deleted or updated. For example, if you update a row, you'll see a delete version and an insert version in exhaustive changes, but only the insert version in deduped changed rows. Depending on your use case, you can choose the option that suits your needs. The default option is false, which means exhaustive changes. | No | Boolean | netChanges |
 | Include system Columns | When using snowflake change tracking, you can use the systemColumns option to control whether the metadata stream columns provided by Snowflake are included or excluded in the change tracking output. By default, systemColumns is set to true, which means the metadata stream columns are included. You can set systemColumns to false if you want to exclude them. | No | Boolean | systemColumns |
 | Start reading from beginning | Setting this option with incremental extract and change tracking will instruct ADF to read all rows on first execution of a pipeline with incremental extract turned on. | No | Boolean | skipInitialLoad |
 
@@ -574,12 +574,12 @@ 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's 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
 
-The below table lists the properties supported by Snowflake sink. You can edit these properties in the **Settings** tab. When using inline dataset, you will see additional settings, which are the same as the properties described in [dataset properties](#dataset-properties) section. The connector utilizes Snowflake [internal data transfer](https://docs.snowflake.com/en/user-guide/spark-connector-overview.html#internal-data-transfer).
+The below table lists the properties supported by Snowflake sink. You can edit these properties in the **Settings** tab. When using inline dataset, you'll see additional settings, which are the same as the properties described in [dataset properties](#dataset-properties) section. The connector utilizes Snowflake [internal data transfer](https://docs.snowflake.com/en/user-guide/spark-connector-overview.html#internal-data-transfer).
 
 | Name | Description | Required | Allowed values | Data flow script property |
 | ---- | ----------- | -------- | -------------- | ---------------- |
