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Copy file name to clipboardExpand all lines: articles/iot-operations/connect-to-cloud/concept-dataflow-graphs.md
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@@ -41,15 +41,15 @@ For new projects that use supported endpoint types, we recommend data flow graph
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Each transform is a pre-built processing step that you configure with rules and chain with other transforms inside a `DataflowGraph` resource.
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<!--| Transform | What it does | Learn more |
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| Transform | What it does | Learn more |
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|-----------|-------------|------------|
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|**Map**| Rename, restructure, compute, and copy fields |[Transform data with map](howto-dataflow-graphs-map.md)|
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|**Filter**| Drop messages that match a condition |[Filter and route data](howto-dataflow-graphs-filter-route.md)|
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|**Branch**| Route each message to a `true` or `false` path based on a condition |[Filter and route data](howto-dataflow-graphs-filter-route.md#branch-transform)|
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|**Concat**| Merge two or more paths back into one |[Filter and route data](howto-dataflow-graphs-filter-route.md#merge-paths-with-concat)|
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| **Window** | Collect messages over a time interval, then aggregate | [Aggregate data over time](howto-dataflow-graphs-window.md) |-->
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|**Window**| Collect messages over a time interval, then aggregate |[Aggregate data over time](howto-dataflow-graphs-window.md)|
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<!--All transforms share an [expression language](concept-dataflow-graphs-expressions.md) for operators, functions, and field references. You can also [enrich](howto-dataflow-graphs-enrich.md) messages with external data from a state store in map, filter, and branch transforms.-->
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All transforms share an [expression language](concept-dataflow-graphs-expressions.md) for operators, functions, and field references. You can also [enrich](howto-dataflow-graphs-enrich.md) messages with external data from a state store in map, filter, and branch transforms.
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## How transforms compose
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# [Operations experience](#tab/portal)
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<!-- -->
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In the Operations experience:
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The pipeline defines three elements: a source, a transform (indicated by `nodeType: Graph`), and a destination. The connections describe how data flows between them. The transform's `configuration` passes rules as a JSON string under the `rules` key.
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<!-- In the how-to articles that follow, examples focus on the transform rules themselves. For a step-by-step guide to creating a data flow graph, see [Create a data flow graph](howto-create-dataflow-graph.md). -->
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In the how-to articles that follow, examples focus on the transform rules themselves. For a step-by-step guide to creating a data flow graph, see [Create a data flow graph](howto-create-dataflow-graph.md).
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## Built-in transforms vs. WASM transforms
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## Next steps
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<!-- - [Data flows vs. data flow graphs](overview-dataflow-comparison.md)
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- [Data flows vs. data flow graphs](overview-dataflow-comparison.md)
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- [Create a data flow graph](howto-create-dataflow-graph.md)
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- [Transform data with map](howto-dataflow-graphs-map.md)
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- [Filter and route data](howto-dataflow-graphs-filter-route.md)
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Copy file name to clipboardExpand all lines: articles/iot-operations/connect-to-cloud/howto-configure-dataflow-destination.md
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This approach is more flexible than source topic routing because it lets you set the destination topic based on any field or computed value in the message, not just the source topic structure.
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<!-- For more information and complete examples, see [Route messages to different topics](howto-dataflow-graphs-topic-routing.md). -->
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For more information and complete examples, see [Route messages to different topics](howto-dataflow-graphs-topic-routing.md).
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## Serialize the output with a schema
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# [Azure CLI](#tab/cli)
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<!-- After you [upload a schema to the schema registry](concept-schema-registry.md#upload-a-schema), reference it in the data flow configuration. -->
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After you [upload a schema to the schema registry](concept-schema-registry.md#upload-a-schema), reference it in the data flow configuration.
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```json
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{
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# [Bicep](#tab/bicep)
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<!-- After you [upload a schema to the schema registry](concept-schema-registry.md#upload-a-schema), reference it in the data flow configuration. -->
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After you [upload a schema to the schema registry](concept-schema-registry.md#upload-a-schema), reference it in the data flow configuration.
Copy file name to clipboardExpand all lines: articles/iot-operations/connect-to-cloud/howto-configure-dataflow-source.md
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The source is where data enters a data flow or data flow graph. You configure the source by specifying an endpoint reference and a list of data sources (topics) for that endpoint.
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<!--This page applies to both [data flows](overview-dataflow.md) and [data flow graphs](concept-dataflow-graphs.md). For data flows, the source is an operation in the `Dataflow` resource. For data flow graphs, the source is a `Source` node in the `DataflowGraph` resource.-->
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This page applies to both [data flows](overview-dataflow.md) and [data flow graphs](concept-dataflow-graphs.md). For data flows, the source is an operation in the `Dataflow` resource. For data flow graphs, the source is a `Source` node in the `DataflowGraph` resource.
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> [!IMPORTANT]
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> Data flows support MQTT and Kafka source endpoints. Data flow graphs support MQTT, Kafka, and OpenTelemetry source endpoints. Each data flow must have the Azure IoT Operations local MQTT broker default endpoint as either the source or destination. For more information, see [Data flows must use local MQTT broker endpoint](./howto-configure-dataflow-endpoint.md#data-flows-must-use-local-mqtt-broker-endpoint).
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## Next steps
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<!-- - [Configure a data flow destination](howto-configure-dataflow-destination.md)
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- [Configure a data flow destination](howto-configure-dataflow-destination.md)
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