Comment out offending lines temporarily

Author: sethmanheim

articles/iot-operations/connect-to-cloud/concept-dataflow-enrich.md

@@ -15,8 +15,8 @@ ms.service: azure-iot-operations
 
 [!INCLUDE [kubernetes-management-preview-note](../includes/kubernetes-management-preview-note.md)]
 
-> [!TIP]
-> Data flow graphs support enrichment with expanded capabilities including enrichment in filter and branch transforms. For new projects that use MQTT, Kafka, or OpenTelemetry endpoints, see [Enrich with external data in data flow graphs](howto-dataflow-graphs-enrich.md) (preview).
+<!-- > [!TIP]
+> Data flow graphs support enrichment with expanded capabilities including enrichment in filter and branch transforms. For new projects that use MQTT, Kafka, or OpenTelemetry endpoints, see [Enrich with external data in data flow graphs](howto-dataflow-graphs-enrich.md) (preview). -->
 
 You can enrich data by using the *contextualization datasets* function. When incoming records are processed, you can query these datasets based on conditions that relate to the fields of the incoming record. This capability allows for dynamic interactions. Data from these datasets can be used to supplement information in the output fields and participate in complex calculations during the mapping process.
 

articles/iot-operations/connect-to-cloud/concept-dataflow-mapping.md

@@ -16,8 +16,8 @@ ms.service: azure-iot-operations
 
 [!INCLUDE [kubernetes-management-preview-note](../includes/kubernetes-management-preview-note.md)]
 
-> [!TIP]
-> Data flow graphs offer an expanded mapping language with additional functions, composable transforms, and features like conditional routing and time-based aggregation. For new projects that use MQTT, Kafka, or OpenTelemetry endpoints, see [Transform data with map in data flow graphs](howto-dataflow-graphs-map.md) (preview).
+<!-- > [!TIP]
+> Data flow graphs offer an expanded mapping language with additional functions, composable transforms, and features like conditional routing and time-based aggregation. For new projects that use MQTT, Kafka, or OpenTelemetry endpoints, see [Transform data with map in data flow graphs](howto-dataflow-graphs-map.md) (preview). -->
 
 Use the data flow mapping language to transform data in Azure IoT Operations. The syntax is a simple, yet powerful, way to define mappings that transform data from one format to another. This article provides an overview of the data flow mapping language and key concepts.
 
@@ -958,7 +958,7 @@ In this example, the last known value of `Temperature` is tracked. If a subseque
 
 ## Related content
 
-- [Expressions reference](concept-dataflow-graphs-expressions.md) - Operators, functions, data types, and type conversion rules for all data flow transforms.
-- [Filter data in a data flow](howto-dataflow-filter.md)
+<!-- - [Expressions reference](concept-dataflow-graphs-expressions.md) - Operators, functions, data types, and type conversion rules for all data flow transforms.
+- [Filter data in a data flow](howto-dataflow-filter.md) -->
 - [Enrich data by using data flows](concept-dataflow-enrich.md)
 - [Create a data flow](howto-create-dataflow.md)

articles/iot-operations/connect-to-cloud/concept-schema-registry.md

@@ -25,8 +25,8 @@ Data flows use schemas in three places:
 - **Transformation**: The operations experience uses the source schema as a starting point when you build transformations.
 - **Destination**: Specify an output schema and serialization format when sending data to storage endpoints.
 
-> [!NOTE]
-> For data flow graphs, schemas are configured differently. See [Use schemas in data flow graphs](concept-dataflow-graphs-schema.md).
+<!-- > [!NOTE]
+> For data flow graphs, schemas are configured differently. See [Use schemas in data flow graphs](concept-dataflow-graphs-schema.md). -->
 
 ## Schema formats
 
@@ -85,15 +85,15 @@ Asset sources have a predefined schema created by the connector for OPC UA. For
 
 :::image type="content" source="./media/concept-schema-registry/upload-schema.png" alt-text="Screenshot that shows uploading a message schema in the operations experience web UI.":::
 
-To reference a schema in your data flow source configuration, use the `schemaRef` field. For more information, see [Configure a data flow source](howto-configure-dataflow-source.md#specify-source-schema).
+<!-- To reference a schema in your data flow source configuration, use the `schemaRef` field. For more information, see [Configure a data flow source](howto-configure-dataflow-source.md#specify-source-schema). -->
 
 ## Configure an output schema
 
 Output schemas control how data is serialized before it reaches the destination. Storage endpoints (ADLS Gen2, Fabric OneLake, Azure Data Explorer, local storage) require a schema and support Parquet and Delta serialization formats. MQTT and Kafka destinations use JSON by default.
 
 In the operations experience, when you select a storage destination, the UI applies any transformations to the source schema and generates a Delta schema automatically. The generated schema is stored in the schema registry and referenced by the data flow.
 
-For Bicep or Kubernetes deployments, specify the schema and serialization format in the transformation settings. For more information, see [Configure a data flow destination](howto-configure-dataflow-destination.md#serialize-the-output-with-a-schema).
+<!-- For Bicep or Kubernetes deployments, specify the schema and serialization format in the transformation settings. For more information, see [Configure a data flow destination](howto-configure-dataflow-destination.md#serialize-the-output-with-a-schema). -->
 
 ## Upload a schema
 
@@ -181,7 +181,7 @@ az deployment group create --resource-group <RESOURCE_GROUP> --template-file sch
 
 ## Related content
 
-- [Use schemas in data flow graphs](concept-dataflow-graphs-schema.md)
+<!-- - [Use schemas in data flow graphs](concept-dataflow-graphs-schema.md)
 - [Configure a data flow source](howto-configure-dataflow-source.md)
-- [Configure a data flow destination](howto-configure-dataflow-destination.md)
+- [Configure a data flow destination](howto-configure-dataflow-destination.md) -->
 - [Create a data flow](howto-create-dataflow.md)

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

@@ -32,10 +32,10 @@ Use the following table to choose the endpoint type to configure:
 > [!IMPORTANT]
 > **Data flow graphs limitation**: [Data flow graphs (WASM)](howto-dataflow-graph-wasm.md) currently only support MQTT, Kafka, and OpenTelemetry endpoints. OpenTelemetry endpoints can only be used as destinations in data flow graphs. Other endpoint types are not supported for data flow graphs. For more information, see [Known issues](../troubleshoot/known-issues.md#data-flow-graphs-only-support-specific-endpoint-types).
 
-> [!IMPORTANT]
+<!-- > [!IMPORTANT]
 > Storage endpoints require a [schema for serialization](./concept-schema-registry.md). To use data flow with Microsoft Fabric OneLake, Azure Data Lake Storage, Azure Data Explorer, or Local Storage, you must [specify a schema reference](./howto-configure-dataflow-destination.md#serialize-the-output-with-a-schema).
 > 
-> To generate the schema from a sample data file, use the [Schema Gen Helper](https://azure-samples.github.io/explore-iot-operations/schema-gen-helper/).
+> To generate the schema from a sample data file, use the [Schema Gen Helper](https://azure-samples.github.io/explore-iot-operations/schema-gen-helper/). -->
 
 ## Data flows must use local MQTT broker endpoint
 

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

@@ -1004,8 +1004,8 @@ kubectl create configmap client-ca-configmap --from-file root_ca.crt -n azure-io
 
 The consumer group ID is used to identify the consumer group that the data flow uses to read messages from the Kafka topic. The consumer group ID must be unique within the Kafka broker. 
 
-> [!IMPORTANT]
-> When the Kafka endpoint is used as [source](howto-configure-dataflow-source.md), the consumer group ID is required. Otherwise, the data flow can't read messages from the Kafka topic, and you get an error "Kafka type source endpoints must have a consumerGroupId defined".
+<!-- > [!IMPORTANT]
+> When the Kafka endpoint is used as [source](howto-configure-dataflow-source.md), the consumer group ID is required. Otherwise, the data flow can't read messages from the Kafka topic, and you get an error "Kafka type source endpoints must have a consumerGroupId defined". -->
 
 # [Operations experience](#tab/portal)
 

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

@@ -181,7 +181,7 @@ To write your data to the cloud, follow the instructions in [Cloud Ingest Edge V
 
 Then, when configuring your local storage data flow endpoint, input the PVC name under `persistentVolumeClaimRef`.
 
-Finally, when you create the data flow, the [data destination](howto-configure-dataflow-destination.md#configure-the-data-destination-topic-container-or-table) parameter must match the `spec.path` parameter you created for your subvolume during configuration.
+<!-- Finally, when you create the data flow, the [data destination](howto-configure-dataflow-destination.md#configure-the-data-destination-topic-container-or-table) parameter must match the `spec.path` parameter you created for your subvolume during configuration. -->
 
 ## Next steps
 

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

@@ -229,13 +229,13 @@ Review the following sections to learn how to configure the operation types of t
 
 Configure the source endpoint and data sources (topics) for the data flow. You can use the default MQTT broker, an asset, or a custom MQTT or Kafka endpoint as the source.
 
-For complete configuration details, including MQTT topic wildcards, shared subscriptions, Kafka topics, and source schema, see [Configure a data flow source](howto-configure-dataflow-source.md).
+<!-- For complete configuration details, including MQTT topic wildcards, shared subscriptions, Kafka topics, and source schema, see [Configure a data flow source](howto-configure-dataflow-source.md). -->
 
 If you don't use the default endpoint as the source, you must use it as the [destination](#destination). For more information about using the local MQTT broker endpoint, see [Data flows must use local MQTT broker endpoint](./howto-configure-dataflow-endpoint.md#data-flows-must-use-local-mqtt-broker-endpoint).
 
 ## Request disk persistence
 
-Disk persistence keeps data flow processing state across restarts. For configuration details, see [Configure disk persistence](howto-configure-disk-persistence.md).
+<!-- Disk persistence keeps data flow processing state across restarts. For configuration details, see [Configure disk persistence](howto-configure-disk-persistence.md). -->
 
 ## Transformation
 
@@ -388,7 +388,7 @@ For more information about condition syntax, see [Enrich data by using data flow
 
 Use the filter stage to drop messages that don't meet a condition. You can define multiple filter rules with input fields and boolean expressions.
 
-For complete configuration details and examples, see [Filter data in a data flow](howto-dataflow-filter.md).
+<!-- For complete configuration details and examples, see [Filter data in a data flow](howto-dataflow-filter.md). -->
 
 ### Map: Move data from one field to another
 
@@ -693,18 +693,18 @@ To learn more, see [Map data by using data flows](concept-dataflow-mapping.md).
 
 ### Serialize data according to a schema
 
-If you want to serialize the data before sending it to the destination, specify a schema and serialization format. For details, see [Serialize the output with a schema](howto-configure-dataflow-destination.md#serialize-the-output-with-a-schema).
+<!-- If you want to serialize the data before sending it to the destination, specify a schema and serialization format. For details, see [Serialize the output with a schema](howto-configure-dataflow-destination.md#serialize-the-output-with-a-schema). -->
 
 ## Destination
 
 Configure the destination endpoint and data destination (topic, container, or table) for the data flow. You can use any supported endpoint type as the destination, including MQTT, Kafka, Azure Data Lake Storage, Microsoft Fabric, Azure Data Explorer, and local storage.
 
-For complete configuration details, including the data destination table, dynamic destination topics, and output serialization, see [Configure a data flow destination](howto-configure-dataflow-destination.md).
+<!-- For complete configuration details, including the data destination table, dynamic destination topics, and output serialization, see [Configure a data flow destination](howto-configure-dataflow-destination.md). -->
 
 To send data to a destination other than the local MQTT broker, create a data flow endpoint. To learn how, see [Configure data flow endpoints](howto-configure-dataflow-endpoint.md).
 
-> [!IMPORTANT]
-> Storage endpoints require a [schema for serialization](./concept-schema-registry.md). To use data flow with Microsoft Fabric OneLake, Azure Data Lake Storage, Azure Data Explorer, or Local Storage, you must [specify a schema reference](howto-configure-dataflow-destination.md#serialize-the-output-with-a-schema).
+<!-- > [!IMPORTANT]
+> Storage endpoints require a [schema for serialization](./concept-schema-registry.md). To use data flow with Microsoft Fabric OneLake, Azure Data Lake Storage, Azure Data Explorer, or Local Storage, you must [specify a schema reference](howto-configure-dataflow-destination.md#serialize-the-output-with-a-schema). -->
 
 ## Example
 

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

@@ -15,10 +15,10 @@ ai-usage: ai-assisted
 
 [!INCLUDE [kubernetes-management-preview-note](../includes/kubernetes-management-preview-note.md)]
 
-Azure IoT Operations [data flow graphs](concept-dataflow-graphs.md) include built-in transforms for common processing tasks like mapping, filtering, and aggregation. When you need custom logic beyond what the built-in transforms provide, you can deploy WebAssembly (WASM) modules as custom transforms in your data flow graph pipelines.
+<!-- Azure IoT Operations [data flow graphs](concept-dataflow-graphs.md) include built-in transforms for common processing tasks like mapping, filtering, and aggregation. When you need custom logic beyond what the built-in transforms provide, you can deploy WebAssembly (WASM) modules as custom transforms in your data flow graph pipelines.
 
 > [!TIP]
-> For most data processing scenarios, start with the [built-in transforms](concept-dataflow-graphs.md#available-transforms). Use WASM transforms when you need custom business logic, specialized algorithms, or processing that the built-in options don't cover.
+> For most data processing scenarios, start with the [built-in transforms](concept-dataflow-graphs.md#available-transforms). Use WASM transforms when you need custom business logic, specialized algorithms, or processing that the built-in options don't cover. -->
 
 > [!TIP]
 > Want to run AI in-band? See [Run ONNX inference in WebAssembly data flow graphs](../develop-edge-apps/howto-wasm-onnx-inference.md) to package and execute small ONNX models inside your WASM operators.
@@ -33,8 +33,8 @@ Azure IoT Operations [data flow graphs](concept-dataflow-graphs.md) include buil
   - **Quick start with public registry**: Create a registry endpoint pointing to `ghcr.io/azure-samples/explore-iot-operations` with anonymous authentication. For instructions, see [Use prebuilt modules from a public registry](../develop-edge-apps/howto-deploy-wasm-graph-definitions.md#use-prebuilt-modules-from-a-public-registry).
   - **Private registry**: Set up your own container registry and push the sample modules by following guidance in [Deploy WebAssembly (WASM) modules and graph definitions](../develop-edge-apps/howto-deploy-wasm-graph-definitions.md).
 
-> [!NOTE]
-> **Data flows vs. data flow graphs**: A *data flow* is a pipeline that moves and transforms data between endpoints by using built-in transformations. A *data flow graph* extends data flows with composable processing steps. Azure IoT Operations provides [built-in data flow graphs](concept-dataflow-graphs.md) for common operations like mapping, filtering, branching, and aggregation. For custom processing logic, you can implement WebAssembly modules as described in this article. Data flow graphs use YAML graph definitions that specify how operators connect. The data flow graph resource wraps this definition and maps its abstract source and sink operations to concrete endpoints, like MQTT topics and Kafka topics.
+<!-- > [!NOTE]
+> **Data flows vs. data flow graphs**: A *data flow* is a pipeline that moves and transforms data between endpoints by using built-in transformations. A *data flow graph* extends data flows with composable processing steps. Azure IoT Operations provides [built-in data flow graphs](concept-dataflow-graphs.md) for common operations like mapping, filtering, branching, and aggregation. For custom processing logic, you can implement WebAssembly modules as described in this article. Data flow graphs use YAML graph definitions that specify how operators connect. The data flow graph resource wraps this definition and maps its abstract source and sink operations to concrete endpoints, like MQTT topics and Kafka topics. -->
 
 ## Overview
 

articles/iot-operations/connect-to-cloud/overview-dataflow.md

@@ -45,8 +45,8 @@ You can apply transformations to data during the processing stage to perform var
 - **Standardizing values**: Scale property values to a user-defined range.
 - **Contextualizing data**: Add reference data to messages for enrichment and driving insights.
 
-> [!TIP]
-> For richer processing capabilities including conditional routing, time-based aggregation, and composable transform pipelines, see [Data flow graphs](concept-dataflow-graphs.md) (preview).
+<!-- > [!TIP]
+> For richer processing capabilities including conditional routing, time-based aggregation, and composable transform pipelines, see [Data flow graphs](concept-dataflow-graphs.md) (preview). -->
 
 ### Configuration and deployment
 
@@ -77,8 +77,8 @@ The local MQTT broker message queue is stored in memory by default. You can conf
 
 ## Related content
 
-- [Data flows vs. data flow graphs](overview-dataflow-comparison.md)
-- [Data flow graphs overview](concept-dataflow-graphs.md)
+<!-- - [Data flows vs. data flow graphs](overview-dataflow-comparison.md)
+- [Data flow graphs overview](concept-dataflow-graphs.md) -->
 - [Create a data flow](howto-create-dataflow.md)
 - [Configure a data flow source](howto-configure-dataflow-source.md)
 - [Create a data flow endpoint](howto-configure-dataflow-endpoint.md)