Remove preview notes

Author: dominicbetts

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

@@ -16,7 +16,7 @@ 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).
+> 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).
 
 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-graphs.md

@@ -33,7 +33,6 @@ Azure IoT Operations provides two ways to process data in a pipeline:
 | Time-based aggregation | Not available | Window transforms with tumbling windows |
 | Conditional routing | Not available | Branch and concat transforms |
 | Endpoint support | All endpoint types | MQTT, Kafka, and OpenTelemetry only |
-| Status | Generally available | Preview |
 
 For new projects that use supported endpoint types, we recommend data flow graphs. Data flows remain fully supported for all scenarios, and they support the full range of endpoint types.
 

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

@@ -17,7 +17,7 @@ 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).
+> 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).
 
 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.
 

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

@@ -26,7 +26,7 @@ Data flows are generally available and support all endpoint types.
 
 A [data flow graph](concept-dataflow-graphs.md) is a composable, graph-based pipeline that connects multiple transforms in any topology you define. You can chain, branch, and merge processing steps. Each transform is a pre-built processing unit (map, filter, branch, window, enrichment) that you configure with rules. You configure a data flow graph by creating a `DataflowGraph` custom resource.
 
-Data flow graphs are in preview and support MQTT, Kafka, and OpenTelemetry endpoints.
+Data flow graphs support MQTT, Kafka, and OpenTelemetry endpoints.
 
 ## Comparison
 
@@ -41,7 +41,6 @@ Data flow graphs are in preview and support MQTT, Kafka, and OpenTelemetry endpo
 | Dynamic destination topics | `${inputTopic}` (source topic passthrough) | `$metadata.topic` + `${outputTopic}` (content-based routing) |
 | Schema | On source and transformation | On node connections |
 | Disk persistence | Supported | Supported |
-| Status | Generally available | Preview |
 
 ## Shared infrastructure
 

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

@@ -46,7 +46,7 @@ You can apply transformations to data during the processing stage to perform var
 - **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).
+> For richer processing capabilities including conditional routing, time-based aggregation, and composable transform pipelines, see [Data flow graphs](concept-dataflow-graphs.md).
 
 ### Configuration and deployment