two-schema-registry-articles

Author: LHL407

articles/event-hubs/schema-registry-client-side-enforcement.md

@@ -16,23 +16,23 @@ This flow is illustrated as shown:
 :::image type="content" source="./media/schema-registry-overview/information-flow.svg" alt-text="Image showing the Schema Registry information flow." border="false":::
 
 > [!NOTE]
-> While the diagram showcases the information flow when event producers and consumers use Schema Registry with the **Kafka** protocol and **Avro** schema, it doesn't really change for other protocols and schema formats.
+> While the diagram showcases the information flow when event producers and consumers use Schema Registry with the Kafka protocol and Avro schema, it doesn't really change for other protocols and schema formats.
 >
 
 ### Producer
 
 1. Kafka producer application uses `KafkaAvroSerializer` to serialize event data using the specified schema. Producer application provides details of the schema registry endpoint and other optional parameters that are required for schema validation.
 
-2. The serializer looks for the schema in the schema registry to serialize event data. If it finds the schema, then the corresponding schema ID is returned. You can configure the producer application to auto register the schema with the schema registry if it doesn't exist.
+1. The serializer looks for the schema in the schema registry to serialize event data. If it finds the schema, then the corresponding schema ID is returned. You can configure the producer application to auto register the schema with the schema registry if it doesn't exist.
 
-3. Then the serializer prepends the schema ID to the serialized data that is published to the Event Hubs.
+1. Then the serializer prepends the schema ID to the serialized data that is published to the Event Hubs.
 
 ### Consumer
 
 1. Kafka consumer application uses `KafkaAvroDeserializer` to deserialize data that it receives from the event hub.
 
-2. The deserializer uses the schema ID (prepended by the producer) to retrieve schema from the schema registry.
+1. The deserializer uses the schema ID (prepended by the producer) to retrieve schema from the schema registry.
 
-3. The deserializer uses the schema to deserialize event data that it receives from the event hub.
+1. The deserializer uses the schema to deserialize event data that it receives from the event hub.
 
-4. The schema registry client uses caching to prevent redundant schema registry lookups in the future.  
+1. The schema registry client uses caching to prevent redundant schema registry lookups in the future.  

articles/event-hubs/schema-registry-concepts.md

@@ -9,49 +9,49 @@ ms.author: spelluru
 
 #  Schema Registry in Azure Event Hubs
 
-Schema Registry is crucial in loosely coupled and event streaming workflows for maintaining data consistency, simplifying schema evolution, enhancing interoperability, and reducing development effort. It ensures highly reliable data processing and governance with little operational overhead in large distributed organizations with a centralized repository for schemas.
+To maintain data consistency, simplify schema evolution, enhance interoperability, and reduce development effort, using schema registries is crucial in loosely coupled and event streaming workflows. It ensures highly reliable data processing and governance with little operational overhead in large distributed organizations with a centralized repository for schemas.
 
 Schema Registry in Azure Event Hubs fulfills multiple roles in schema-driven event streaming scenarios:
-   * Provides a repository where multiple schemas can be registered, managed, and evolved.
-   * Managed schema evolution with multiple compatibility rules.
-   * Performs data validation for all schematized data.
-   * Provides client-side libraries (serializers and deserializers) for producers and consumers.
-   * Improves network throughput efficiency by passing schema ID instead of the schema definition for every payload.
+   * Provides a repository where multiple schemas can be registered, managed, and evolved
+   * Manages schema evolution with multiple compatibility rules
+   * Performs data validation for all schematized data
+   * Provides client-side libraries (serializers and deserializers) for producers and consumers
+   * Improves network throughput efficiency by passing schema ID instead of the schema definition for every payload
 
 Schema Registry is supported on Standard, Premium, and Dedicated tiers.
 
 ## Schema Registry components
 
-The Schema Registry lives in the context of the Event Hubs namespace, but it can be used with all Azure messaging service or other message or events broker. It comprises multiple schema groups which act as a logical grouping of schemas and can be managed independent of other schema groups. 
+The Schema Registry lives in the context of the Event Hubs namespace, but it can be used with all Azure messaging service or other message or events broker. It comprises multiple schema groups which act as a logical grouping of schemas and can be managed independent of other schema groups.
 
 :::image type="content" source="./media/schema-registry-overview/elements.png" alt-text="Diagram that shows the components of Schema Registry in Azure Event Hubs." border="false":::
 
 ### Schemas
 
-In any loosely coupled system, there are multiple applications communicating with each other, primarily through data. Schemas act as a declarative way to define the structure of the data so that the contract between these producer and consumer applications is well defined, ensuring reliable processing at scale.
+In any loosely coupled system, there are multiple applications communicating with each other, primarily through data. Schemas act as a declarative way to define the structure of the data so that the contract between producer and consumer applications is well-defined, ensuring reliable processing at scale.
 
-A schema definition includes -
-   * Fields - name of the individual data elements (that is, first/last name, book title, address).
-   * Data types - the kind of data that can be stored in each field (for example, string, date-time, array).
-   * Structure - the organization of the different fields (that is, nested structures or arrays).
+A schema definition includes:
+   * Fields: name of the individual data elements (that is, first/last name, book title, address)
+   * Data types: the kind of data that can be stored in each field (for example, string, date-time, array)
+   * Structure: the organization of the different fields (that is, nested structures or arrays)
 
 Schemas define the contract between producers and consumers. A schema defined in an Event Hubs schema registry helps manage the contract outside of event data, thus removing the payload overhead.
 
 #### Schema formats 
 Schema formats are used to determine the manner in which a schema is structured and defined, with each format outlining specific guidelines and syntax for defining the structure of the events that will be used for event streaming.
 
 ##### Avro schema 
-[Avro](https://avro.apache.org/) is a popular data serialization system that uses a compact binary format and provides schema evolution capabilities. 
+[Avro](https://avro.apache.org/) is a popular data serialization system that uses a compact binary format and provides schema evolution capabilities.
 
 To learn more about using Avro schema format with Event Hubs Schema Registry, see:  
-- [How to use schema registry with Kafka and Avro](schema-registry-kafka-java-send-receive-quickstart.md)
-- [How to use Schema registry with Event Hubs .NET SDK (AMQP) and Avro.](schema-registry-dotnet-send-receive-quickstart.md)
+* [How to use schema registry with Kafka and Avro](schema-registry-kafka-java-send-receive-quickstart.md)
+* [How to use Schema registry with Event Hubs, .NET, SDK (AMQP), and Avro.](schema-registry-dotnet-send-receive-quickstart.md)
 
 ##### JSON Schema
-[JSON Schema](https://json-schema.org/) is a standardized way of defining the structure and data types of the events. JSON Schema enables the confident and reliable use of the JSON data format in event streaming.
+[JSON (JavaScript Object Notation) Schema](https://json-schema.org/) is a standardized way of defining the structure and data types of the events. JSON Schema enables the confident and reliable use of the JSON data format in event streaming.
 
 To learn more about using JSON schema format with Event Hubs Schema Registry, see:  
-- [How to use schema registry with Kafka and JSON Schema](schema-registry-json-schema-kafka.md)
+* [How to use schema registry with Kafka and JSON Schema](schema-registry-json-schema-kafka.md)
 
 ##### Protobuf
 
@@ -60,9 +60,9 @@ To learn more about using JSON schema format with Event Hubs Schema Registry, se
 ### Schema groups
 
 Schema groups are logical groups of similar schemas based on your business criteria. A schema group holds:
-   * multiple schema definition, 
-   * multiple versions of a specific schema, and
-   * metadata regarding the schema type and compatibility for all schemas in the group.
+   * Multiple schema definitions 
+   * Multiple versions of a specific schema
+   * Metadata regarding the schema type and compatibility for all schemas in the group
 
 A schema groups can be thought of as a subset of the schema registry, aligned with a particular application or organizational unit, with a separate authorization model. This extra security boundary ensures that in the shared services model, metadata, and trade secrets aren't leaked. It also allows for application owners to manage schemas independent of other applications that share the same namespace.
 
@@ -75,30 +75,30 @@ Schemas need to evolve with the business requirement of producers and consumers.
 Azure Schema Registry for Event Hubs support following compatibility modes.
 
 ### Backward compatibility
-Backward compatibility mode allows the consumer code to use a new version of schema but it can process messages with old version of the schema. When you use backward compatibility mode in a schema group, it allows following changes to be made on a schema.
+Backward compatibility mode allows the consumer code to use a new version of a schema but it can process messages with old version of the schema. When you use backward compatibility mode in a schema group, it allows following changes to be made on a schema.
 
-* Delete fields.
-* Add optional fields.
+* Delete fields
+* Add optional fields
 
 ### Forward compatibility
 Forward compatibility allows the consumer code to use an old version of the schema but it can read messages with the new schema. Forward compatibility mode allows following changes to be made on a schema.
-* Add fields 
+* Add fields
 * Delete optional fields
 
 ### No compatibility
-When the ``None`` compatibility mode is used, the schema registry doesn't do any compatibility checks when you update schemas. 
+When the ``None`` compatibility mode is used, the schema registry doesn't do any compatibility checks when you update schemas.
 
 ## Client SDKs
 
 You can use one of the following libraries to include an Avro serializer, which you can use to serialize and deserialize payloads containing Schema Registry schema identifiers and Avro-encoded data.
 
-* [.NET - Microsoft.Azure.Data.SchemaRegistry.ApacheAvro](https://github.com/Azure/azure-sdk-for-net/tree/master/sdk/schemaregistry/Microsoft.Azure.Data.SchemaRegistry.ApacheAvro)
-* [Java - azure-data-schemaregistry-avro](https://github.com/Azure/azure-sdk-for-java/tree/main/sdk/schemaregistry/azure-data-schemaregistry-apacheavro)
-* [Python - azure-schemaregistry-avroserializer](https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/schemaregistry/azure-schemaregistry-avroencoder/)
-* [JavaScript - @azure/schema-registry-avro](https://github.com/Azure/azure-sdk-for-js/tree/master/sdk/schemaregistry/schema-registry-avro)
-* [Apache Kafka](https://github.com/Azure/azure-schema-registry-for-kafka/) - Run Kafka-integrated Apache Avro serializers and deserializers backed by Azure Schema Registry. The Java client's Apache Kafka client serializer for the Azure Schema Registry can be used in any Apache Kafka scenario and with any Apache Kafka® based deployment or cloud service. 
-* **Azure CLI** - For an example of adding a schema to a schema group using CLI, see [Adding a schema to a schema group using CLI](https://github.com/Azure/azure-event-hubs/tree/master/samples/Management/CLI/AddschematoSchemaGroups).
-* **PowerShell** - For an example of adding a schema to a schema group using PowerShell, see [Adding a schema to a schema group using PowerShell](https://github.com/Azure/azure-event-hubs/tree/master/samples/Management/PowerShell/AddingSchematoSchemagroups).
+* **.NET**: [Microsoft.Azure.Data.SchemaRegistry.ApacheAvro](https://github.com/Azure/azure-sdk-for-net/tree/master/sdk/schemaregistry/Microsoft.Azure.Data.SchemaRegistry.ApacheAvro)
+* **Java**: [azure-data-schemaregistry-avro](https://github.com/Azure/azure-sdk-for-java/tree/main/sdk/schemaregistry/azure-data-schemaregistry-apacheavro)
+* **Python**: [azure-schemaregistry-avroserializer](https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/schemaregistry/azure-schemaregistry-avroencoder/)
+* **JavaScript**: [@azure/schema-registry-avro](https://github.com/Azure/azure-sdk-for-js/tree/master/sdk/schemaregistry/schema-registry-avro)
+* **Apache Kafka**: Run [Kafka-integrated Apache Avro](https://github.com/Azure/azure-schema-registry-for-kafka/) serializers and deserializers backed by Azure Schema Registry. The Java client's Apache Kafka client serializer for the Azure Schema Registry can be used in any Apache Kafka scenario and with any Apache Kafka® based deployment or cloud service.
+* **Azure CLI**: For an example of adding a schema to a schema group using CLI, see [Adding a schema to a schema group using CLI](https://github.com/Azure/azure-event-hubs/tree/master/samples/Management/CLI/AddschematoSchemaGroups).
+* **PowerShell**: For an example of adding a schema to a schema group using PowerShell, see [Adding a schema to a schema group using PowerShell](https://github.com/Azure/azure-event-hubs/tree/master/samples/Management/PowerShell/AddingSchematoSchemagroups).
 
 ## Limits
 For limits (for example: number of schema groups in a namespace) of Event Hubs, see [Event Hubs quotas and limits](event-hubs-quotas.md).
@@ -107,7 +107,7 @@ For limits (for example: number of schema groups in a namespace) of Event Hubs,
 To access a schema registry programmatically, follow these steps:
 
 1. [Register your application in Microsoft Entra ID](../active-directory/develop/quickstart-register-app.md)
-1. Add the security principal of the application to one of the following Azure RBAC(role-based access control) roles at the **namespace** level. 
+1. Add the security principal of the application to one of the following Azure RBAC (role-based access control) roles at the **namespace** level.
 
 | Role | Description |
 | ---- | ----------- |
@@ -116,7 +116,7 @@ To access a schema registry programmatically, follow these steps:
 | [Schema Registry Reader](../role-based-access-control/built-in-roles.md#schema-registry-reader-preview) | Read and list Schema Registry groups and schemas. |
 | [Schema Registry Contributor](../role-based-access-control/built-in-roles.md#schema-registry-reader-preview) | Read, write, and delete Schema Registry groups and schemas. |
 
-For instructions on creating registering an application using the Azure portal, see [Register an app with Microsoft Entra ID](../active-directory/develop/quickstart-register-app.md). Note down the client ID (application ID), tenant ID, and the secret to use in the code. 
+For instructions on creating registering an application using the Azure portal, see [Register an app with Microsoft Entra ID](../active-directory/develop/quickstart-register-app.md). Note down the client ID (application ID), tenant ID, and the secret to use in the code.
 
 ## Next steps