Clarity

Author: PatAltimore

articles/iot-operations/connect-to-cloud/howto-configure-adlsv2-endpoint.md

@@ -132,11 +132,11 @@ If you need to override the system-assigned managed identity audience, see the [
 
 Follow the steps in the [access token](#access-token) section to get a SAS token for the storage account and store it in a Kubernetes secret. 
 
-Then, create the *DataflowEndpoint* resource and specify the access token authentication method. Here, replace `<SAS_SECRET_NAME>` with name of the secret containing the SAS token as well as other placeholder values.
+Then, create the *DataflowEndpoint* resource and specify the access token authentication method. Here, replace `<SAS_SECRET_NAME>` with name of the secret containing the SAS token and other placeholder values.
 
 # [Portal](#tab/portal)
 
-1. In the Azure IoT Operations Preview portal, create a new dataflow or edit an existing dataflow by selecting the **Dataflows** tab on the left. If creating a new dataflow, select a source for the dataflow.
+1. In the Azure IoT Operations Preview portal, create a new dataflow or edit an existing dataflow by selecting the **Dataflows** tab. If creating a new dataflow, select a source for the dataflow.
 1. In the editor, select the destination dataflow endpoint.
 1. Choose the Azure Data Lake Storage Gen2 endpoint that you created previously.
 

articles/iot-operations/connect-to-cloud/howto-configure-fabric-endpoint.md

@@ -255,7 +255,7 @@ You can set advanced settings for the Fabric OneLake endpoint, such as the batch
 
 ### OneLake path type
 
-The `oneLakePathType` setting determines the type of path to use in the OneLake path. The default value is `Tables`, which is the recommended path type for the most common use cases. The `Tables` path type is a table in the OneLake lakehouse that is used to store the data. It can also be set as `Files`, which is a file in the OneLake lakehouse that is used to store the data. The `Files` path type is useful when you want to store the data in a file format that is not supported by the `Tables` path type.
+The `oneLakePathType` setting determines the type of path to use in the OneLake path. The default value is `Tables`, which is the recommended path type for the most common use cases. The `Tables` path type is a table in the OneLake lakehouse that is used to store the data. It can also be set as `Files`, which is a file in the OneLake lakehouse that is used to store the data. The `Files` path type is useful when you want to store the data in a file format that isn't supported by the `Tables` path type.
 
 # [Portal](#tab/portal)
 

articles/iot-operations/connect-to-cloud/howto-configure-kafka-endpoint.md

@@ -284,7 +284,7 @@ spec:
 
 ---
 
-To customize the endpoint settings, see the following sections for more information.
+To customize the endpoint settings, use the following sections for more information.
 
 
 ### Available authentication methods
@@ -293,7 +293,7 @@ The following authentication methods are available for Kafka broker dataflow end
 
 #### SASL
 
-To use SASL for authentication, specify the SASL authentication method and configure SASL type as well as a secret reference with the name of the secret that contains the SASL token.
+To use SASL for authentication, specify the SASL authentication method and configure SASL type and a secret reference with the name of the secret that contains the SASL token.
 
 # [Portal](#tab/portal)
 
@@ -738,8 +738,8 @@ Aside from compression, you can also configure batching for messages before send
 | ----- | ----------- | -------- |
 | `mode` | Enable batching or not. If not set, the default value is Enabled because Kafka doesn't have a notion of *unbatched* messaging. If set to Disabled, the batching is minimized to create a batch with a single message each time. | No |
 | `latencyMs` | The maximum time interval in milliseconds that messages can be buffered before being sent. If this interval is reached, then all buffered messages are sent as a batch, regardless of how many or how large they are. If not set, the default value is 5. | No |
-| `maxMessages` | The maximum number of messages that can be buffered before being sent. If this number is reached, then all buffered messages are sent as a batch, regardless of how large they are or how long they are buffered. If not set, the default value is 100000.  | No |
-| `maxBytes` | The maximum size in bytes that can be buffered before being sent. If this size is reached, then all buffered messages are sent as a batch, regardless of how many they are or how long they are buffered. The default value is 1000000 (1 MB). | No |
+| `maxMessages` | The maximum number of messages that can be buffered before being sent. If this number is reached, then all buffered messages are sent as a batch, regardless of how large or how long they're buffered. If not set, the default value is 100000.  | No |
+| `maxBytes` | The maximum size in bytes that can be buffered before being sent. If this size is reached, then all buffered messages are sent as a batch, regardless of how many or how long they're buffered. The default value is 1000000 (1 MB). | No |
 
 For example, if you set latencyMs to 1000, maxMessages to 100, and maxBytes to 1024, messages are sent either when there are 100 messages in the buffer, or when there are 1,024 bytes in the buffer, or when 1,000 milliseconds elapse since the last send, whichever comes first.
 
@@ -829,20 +829,20 @@ This setting takes effect only if the endpoint is used as a destination (that is
 
 | Value | Description |
 | ----- | ----------- |
-| `None` | The dataflow doesn't wait for any acknowledgments from the Kafka broker. This is the fastest but least durable option. |
-| `All` | The dataflow waits for the message to be written to the leader partition and all follower partitions. This is the slowest but most durable option. This is also the default option|
+| `None` | The dataflow doesn't wait for any acknowledgments from the Kafka broker. This setting is the fastest but least durable option. |
+| `All` | The dataflow waits for the message to be written to the leader partition and all follower partitions. This setting is the slowest but most durable option. This setting is also the default option|
 | `One` | The dataflow waits for the message to be written to the leader partition and at least one follower partition. |
 | `Zero` | The dataflow waits for the message to be written to the leader partition but doesn't wait for any acknowledgments from the followers. This is faster than `One` but less durable. |
 
 <!-- TODO: double check for accuracy -->
 
-For example, if you set the Kafka acknowledgement to `All`, the dataflow waits for the message to be written to the leader partition and all follower partitions before sending the next message.
+For example, if you set the Kafka acknowledgment to `All`, the dataflow waits for the message to be written to the leader partition and all follower partitions before sending the next message.
 
 To configure the Kafka acknowledgments:
 
 # [Portal](#tab/portal)
 
-In the operations experience dataflow endpoint settings page, select the **Advanced** tab then use the **Kafka acknowledgement** field to specify the Kafka acknowledgement level.
+In the operations experience dataflow endpoint settings page, select the **Advanced** tab then use the **Kafka acknowledgment** field to specify the Kafka acknowledgment level.
 
 # [Bicep](#tab/bicep)
 
@@ -915,7 +915,7 @@ When a dataflow receives an MQTT message with the Message Expiry Interval specif
 
 * Records the time the message was received.
 * Before the message is emitted to the destination, time is subtracted from the message has been queued from the original expiry interval time.
- * If the message has not yet expired (the operation above is > 0), then the message is emitted to the destination and contains the updated Message Expiry Time.
+ * If the message hasn't expired (the operation above is > 0), then the message is emitted to the destination and contains the updated Message Expiry Time.
  * If the message has expired (the operation above is <= 0), then the message isn't emitted by the Target.
 
 Examples:
@@ -1003,7 +1003,7 @@ Properties =
 
 The dataflow endpoint discards packets that contain the `"float-value"` field.
 
-Not all event data properties including propertyEventData.correlationId are not forwarded. For more information, see [Event User Properties](https://github.com/Azure/azure-event-hubs-for-kafka/tree/master/tutorials/interop#event-user-properties),
+Not all event data properties including propertyEventData.correlationId are forwarded. For more information, see [Event User Properties](https://github.com/Azure/azure-event-hubs-for-kafka/tree/master/tutorials/interop#event-user-properties),
 
 ### CloudEvents
 

articles/iot-operations/connect-to-cloud/howto-create-dataflow.md

@@ -245,7 +245,7 @@ For more information about the default MQTT endpoint and creating an MQTT endpoi
 
 ### Use custom MQTT or Kafka dataflow endpoint as source
 
-If you created a custom MQTT or Kafka dataflow endpoint (for example, to use with Event Grid or Event Hubs), you can use it as the source for the dataflow. Remember that storage type endpoints, like Data Lake or Fabric OneLake, cannot be used as source.
+If you created a custom MQTT or Kafka dataflow endpoint (for example, to use with Event Grid or Event Hubs), you can use it as the source for the dataflow. Remember that storage type endpoints, like Data Lake or Fabric OneLake, can't be used as source.
 
 To configure, use Kubernetes YAML or Bicep. Replace placeholder values with your custom endpoint name and topics.
 
@@ -511,7 +511,7 @@ You can load sample data into the DSS by using the [DSS set tool sample](https:/
 In the operations experience, the *Enrich* stage is currently supported using the **Rename** and **New property** transforms.
 
 1. In the operations experience, select a dataflow then **Add transform (optional)**.
-1. Chose **Rename** or **New property** transforms then select **Add**.
+1. Choose **Rename** or **New property** transforms then select **Add**.
 
     :::image type="content" source="media/howto-create-dataflow/dataflow-enrich.png" alt-text="Screenshot using operations experience to rename a datapoint and add a new property.":::
 
@@ -589,7 +589,8 @@ builtInTransformationSettings: {
     }
   ]
 }
-``` -->
+``` 
+-->
 
 For more information about condition syntax, see [Enrich data by using dataflows](concept-dataflow-enrich.md) and [Convert data using dataflows](concept-dataflow-conversions.md).
 
@@ -775,7 +776,7 @@ destinationSettings:
 
 ### Configure data destination (topic, container, or table)
 
-Similar to data sources, data destination is a concept that is used to keep the dataflow endpoints reusable across multiple dataflows. Essentially, it represents the sub-directory in the dataflow endpoint configuration. For example, if the dataflow endpoint is a storage endpoint, the data destination is the table in the storage account. If the dataflow endpoint is a Kafka endpoint, the data destination is the Kafka topic.
+Similar to data sources, data destination is a concept that is used to keep the dataflow endpoints reusable across multiple dataflows. Essentially, it represents the subdirectory in the dataflow endpoint configuration. For example, if the dataflow endpoint is a storage endpoint, the data destination is the table in the storage account. If the dataflow endpoint is a Kafka endpoint, the data destination is the Kafka topic.
 
 | Endpoint type | Data destination meaning | Description |
 | - | - | - |
@@ -790,7 +791,7 @@ To configure the data destination:
 
 # [Portal](#tab/portal)
 
-When using the operations experience, the data destination field is automatically interpreted based on the endpoint type. For example, if the dataflow endpoint is a storage endpoint, the destination details page prompts you to enter the container name. If the dataflow endpoint is a MQTT endpoint, the destination details page prompts you to enter the topic, and so on.
+When using the operations experience, the data destination field is automatically interpreted based on the endpoint type. For example, if the dataflow endpoint is a storage endpoint, the destination details page prompts you to enter the container name. If the dataflow endpoint is an MQTT endpoint, the destination details page prompts you to enter the topic, and so on.
 
 :::image type="content" source="media/howto-create-dataflow/data-destination.png" alt-text="Screenshot showing the operations experience prompting the user to enter an MQTT topic given the endpoint type.":::