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

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@@ -106,3 +106,171 @@ Output schemas are associated with dataflow destinations are only used for dataf
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Note: The Delta schema format is used for both Parquet and Delta output.
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For these dataflows, the operations experience applies any transformations to the input schema then creates a new schema in Delta format. When the dataflow custom resource (CR) is created, it includes a `schemaRef` value that points to the generated schema stored in the schema registry.
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To upload an output schema, see [Upload schema](#upload-schema).
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## Upload schema
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Input schema can be uploaded in the operations experience portal as [mentioned previously](#input-schema). You can also upload a schema using a Bicep template.
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### Example with Bicep template
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Create a Bicep `.bicep` file, and add the schema content to it at the top as a variable. This example is a Delta schema that corresponds to the OPC UA data from [quickstart](../get-started-end-to-end-sample/quickstart-add-assets.md).
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```bicep
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// Delta schema content matching OPC UA data from quickstart
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// For ADLS Gen2, ADX, and Fabric destinations
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var opcuaSchemaContent = '''
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{
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"$schema": "Delta/1.0",
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"type": "object",
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"properties": {
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"type": "struct",
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"fields": [
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{
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"name": "temperature",
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"type": {
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"type": "struct",
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"fields": [
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{
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"name": "SourceTimestamp",
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"type": "string",
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"nullable": true,
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"metadata": {}
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},
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{
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"name": "Value",
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"type": "integer",
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"nullable": true,
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"metadata": {}
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},
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{
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"name": "StatusCode",
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"type": {
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"type": "struct",
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"fields": [
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{
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"name": "Code",
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"type": "integer",
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"nullable": true,
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"metadata": {}
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},
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{
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"name": "Symbol",
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"type": "string",
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"nullable": true,
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"metadata": {}
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}
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]
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},
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"nullable": true,
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"metadata": {}
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}
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]
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},
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"nullable": true,
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"metadata": {}
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},
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{
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"name": "Tag 10",
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"type": {
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"type": "struct",
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"fields": [
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{
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"name": "SourceTimestamp",
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"type": "string",
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"nullable": true,
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"metadata": {}
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},
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{
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"name": "Value",
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"type": "integer",
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"nullable": true,
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"metadata": {}
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},
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{
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"name": "StatusCode",
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"type": {
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"type": "struct",
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"fields": [
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{
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"name": "Code",
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"type": "integer",
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"nullable": true,
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"metadata": {}
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},
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{
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"name": "Symbol",
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"type": "string",
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"nullable": true,
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"metadata": {}
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}
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]
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},
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"nullable": true,
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"metadata": {}
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}
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]
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},
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"nullable": true,
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"metadata": {}
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}
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]
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}
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}
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'''
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```
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Then, define schema resource along with pointers to the existing Azure IoT Operation instance, custom location, and schema registry resources that you have from deploying Azure IoT Operations.
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```bicep
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// Replace placeholder values with your actual resource names
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param customLocationName string = '<CUSTOM_LOCATION_NAME>'
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param aioInstanceName string = '<AIO_INSTANCE_NAME>'
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param schemaRegistryName string = '<SCHEMA_REGISTRY_NAME>'
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// Pointers to existing resources from AIO deployment
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resource customLocation 'Microsoft.ExtendedLocation/customLocations@2021-08-31-preview' existing = {
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name: customLocationName
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}
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resource aioInstance 'Microsoft.IoTOperations/instances@2024-08-15-preview' existing = {
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name: aioInstanceName
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}
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resource schemaRegistry 'Microsoft.DeviceRegistry/schemaRegistries@2024-09-01-preview' existing = {
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name: schemaRegistryName
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}
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// Name and version of the schema
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param opcuaSchemaName string = 'opcua-output-delta'
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param opcuaSchemaVer string = '1'
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// Define the schema resource to be created and instantiate a version
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resource opcSchema 'Microsoft.DeviceRegistry/schemaRegistries/schemas@2024-09-01-preview' = {
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parent: schemaRegistry
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name: opcuaSchemaName
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properties: {
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displayName: 'OPC UA Delta Schema'
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description: 'This is a OPC UA delta Schema'
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format: 'Delta/1.0'
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schemaType: 'MessageSchema'
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}
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}
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resource opcuaSchemaVersion 'Microsoft.DeviceRegistry/schemaRegistries/schemas/schemaVersions@2024-09-01-preview' = {
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parent: opcSchema
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name: opcuaSchemaVer
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properties: {
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description: 'Schema version'
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schemaContent: opcuaSchemaContent
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}
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}
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```
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After you've defined the schema content and resources, you can deploy the Bicep template to create the schema in the schema registry.
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```azurecli
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az stack group create --name <DEPLOYMENT_NAME> --resource-group <RESOURCE_GROUP> --template-file <FILE>.bicep
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```
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## Next steps
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- [Create a dataflow](howto-create-dataflow.md)

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

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You can set advanced settings for the Fabric OneLake endpoint, such as the batching latency and message count. You can set these settings in the dataflow endpoint **Advanced** portal tab or within the dataflow endpoint custom resource.
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### OneLake path type
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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.
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# [Kubernetes](#tab/kubernetes)
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```yaml
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fabricOneLakeSettings:
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oneLakePathType: Tables # Or Files
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```
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# [Bicep](#tab/bicep)
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```bicep
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fabricOneLakeSettings: {
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oneLakePathType: 'Tables'
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}
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```
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---
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### Batching
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Use the `batching` settings to configure the maximum number of messages and the maximum latency before the messages are sent to the destination. This setting is useful when you want to optimize for network bandwidth and reduce the number of requests to the destination.
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| Field | Description | Required |

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