Merge pull request #14548 from Court72/component-descriptions

[SCOPED] Quality check: AI-readiness: Component spec alignment - add missing descriptions and purpose [11 AAC articles]

Author: JillGrant615

docs/example-scenario/monitoring/monitoring-observable-systems-media-content.md

@@ -22,13 +22,19 @@ More specifically, these are the elements of the system in the diagram:
 
 ### Components
 
-- [Data Explorer](/azure/data-explorer/data-explorer-overview) is a managed data analytics service for near real time analysis of large volumes of data. Data Explorer is a tool for handling large datasets that require high speed and throughput of data retrieval. This architecture uses Data Explorer to store and query datasets for analysis.
-- [Blob Storage](/azure/well-architected/service-guides/azure-blob-storage) is used to hold raw telemetry. This telemetry can come from your applications and services or from third-party vendors. The data can be treated as transient if you don't need to perform more analysis later. The data from Blob Storage is ingested into Data Explorer clusters.
-- [Azure Event Grid](/azure/well-architected/service-guides/event-grid/reliability) is an event delivery system. It's used to listen to events that are published by Blob Storage. Azure Storage events allow applications to react to events like the creation and deletion of blobs. An Azure function subscribes to events that are published by Event Grid.
-- [Azure Event Hubs](/azure/well-architected/service-guides/azure-databricks-security) is a streaming data ingestion service that you can use to ingest millions of events per second from any source. Event hubs represent the front door, often called an event *ingestor*, for an event pipeline. An event ingestor is a component or service that's located between event publishers and event consumers. It decouples the production of an event stream from the consumption of the events.
-- [Azure Functions](/azure/well-architected/service-guides/azure-functions-security) is a serverless solution that's used to parse and transform data ingested via HTTP and blob endpoints and write to the Data Explorer cluster.
-- [Azure Managed Grafana](/azure/managed-grafana/overview) connects to Data Explorer. In this architecture, it generates charts and dashboards that visualize telemetry data. Azure Managed Grafana provides deep integration with Microsoft Entra ID so that you can implement role-based access to dashboards and views.
-- [Metrics Advisor](/azure/ai-services/metrics-advisor/overview) is a part of Azure AI services. It uses AI to perform data monitoring and anomaly detection in time-series data. Metrics Advisor automates the process of applying models to data and provides a set of APIs and a web-based workspace for data ingestion, anomaly detection, and diagnostics. You can use it even if you have no knowledge of machine learning.
+- [Azure Data Explorer](/azure/data-explorer/data-explorer-overview) is a managed data analytics service for near real-time analysis of large volumes of data. Azure Data Explorer is a tool for handling large datasets that require high speed and throughput of data retrieval. In this architecture, Azure Data Explorer is used to store and query datasets for analysis.
+
+- [Blob Storage](/azure/well-architected/service-guides/azure-blob-storage) is a cloud storage service for unstructured data. This telemetry can come from your applications and services or from non-Microsoft vendors. In this architecture, Blob Storage is the initial landing zone for raw telemetry data from applications, services, or partner vendors. You can treat this data as transient if you don't need to perform more analysis later. The data from Blob Storage is ingested into Azure Data Explorer clusters.
+
+- [Azure Event Grid](/azure/well-architected/service-guides/event-grid/reliability) is an event delivery system that routes events from publishers to subscribers. In this architecture, Event Grid listens to events that Blob Storage publishes. Azure Storage events allow applications to react to events such as the creation and deletion of blobs. An Azure function subscribes to events that Event Grid publishes.
+
+- [Azure Event Hubs](/azure/well-architected/service-guides/azure-databricks-security) is a streaming data ingestion service that can ingest millions of events per second from any source. In this architecture, it serves as the front door, or *event ingestor*, for an event pipeline. An event ingestor is a component or service that's located between event publishers and event consumers. It decouples the production of an event stream from the consumption of the events.
+
+- [Azure Functions](/azure/well-architected/service-guides/azure-functions-security) is a serverless solution that's used to parse and transform data. In this architecture, Azure Functions processes raw telemetry ingested via HTTP and blob endpoints and writes it to the Azure Data Explorer cluster for analysis.
+
+- [Azure Managed Grafana](/azure/managed-grafana/overview) is a managed service that provides Grafana dashboards and visualization capabilities. In this architecture, it connects to Azure Data Explorer to generate charts and dashboards that visualize telemetry data. Azure Managed Grafana provides deep integration with Microsoft Entra ID so that you can implement role-based access to dashboards and views.
+
+- [Metrics Advisor](/azure/ai-services/metrics-advisor/overview) is a part of Azure AI services. It uses AI to perform data monitoring and anomaly detection in time-series data. In this architecture, Metrics Advisor automates the process of applying models to data. It also provides a set of APIs and a web-based workspace for data ingestion, anomaly detection, and diagnostics. You can use it even if you have no knowledge of machine learning.
 
 ### Alternatives
 

docs/example-scenario/monitoring/monitoring-observable-systems-media.yml

@@ -8,6 +8,7 @@ metadata:
   ms.topic: conceptual
   ms.subservice: example-scenario
   ms.custom: arb-integrations
+  ai-usage: ai-assisted
 azureCategories:
   - media
   - ai-machine-learning

docs/example-scenario/serverless/microservices-with-container-apps-content.md

@@ -1,6 +1,6 @@
 This example scenario shows an example of an existing workload that was originally designed to run on Kubernetes can instead run in Azure Container Apps. Azure Container Apps is well-suited for brownfield workloads where teams are looking to simplify complex infrastructure and container orchestration. The example workload runs a containerized microservices application.
 
-The example takes the workload used in [Microservices architecture on Azure Kubernetes Service](../../reference-architectures/containers/aks-microservices/aks-microservices.yml) and rehosts it in Azure Container Apps as its application platform.
+The example takes the workload used in [Microservices architecture on Azure Kubernetes Service (AKS)](../../reference-architectures/containers/aks-microservices/aks-microservices.yml) and rehosts it in Azure Container Apps as its application platform.
 
 > [!TIP]
 >
@@ -44,46 +44,46 @@ The drone delivery service uses a series of Azure services in concert with one a
 
 #### Azure Container Apps
 
-[Azure Container Apps](/azure/container-apps/overview) is the primary component.
+[Azure Container Apps](/azure/well-architected/service-guides/azure-container-apps) is a serverless container platform that simplifies container orchestration and management. In this architecture, Container Apps serves as the primary hosting platform for all microservices.
 
-These features replace many of the complexities of the previous AKS architecture:
+The following features replace many of the complexities of the previous AKS architecture:
 
 - Built-in service discovery
 - Fully managed HTTP and HTTP/2 endpoints
 - Integrated load balancing
 - Logging and monitoring
-- Autoscaling based on HTTP traffic or events powered by KEDA (Kubernetes-based Event Driven Autoscaling)
+- Autoscaling based on HTTP traffic or events powered by Kubernetes-based Event Driven Autoscaling (KEDA)
 - Application upgrades and versioning
 
 #### External storage and other components
 
-**[Azure Key Vault](/azure/key-vault/general/overview)** service for securely storing and accessing secrets, such as API keys, passwords, and certificates.
+- **[Azure Key Vault](/azure/key-vault/general/overview)** is a cloud service for securely storing and accessing secrets, such as API keys, passwords, and certificates. In this architecture, the drone scheduler and delivery services use user-assigned managed identities to authenticate with Key Vault and retrieve secrets.
 
-**[Azure Container Registry](/azure/container-registry/container-registry-intro)** stores private container images. You can also use other container registries like Docker Hub.
+- **[Azure Container Registry](/azure/container-registry/container-registry-intro)** is a managed registry service for storing and managing private container images. In this architecture, it's the source for all container images that are deployed to the Container Apps environment. You can also use other container registries like Docker Hub.
 
-**[Azure Cosmos DB](/azure/well-architected/service-guides/cosmos-db)** stores data using the open-source [Azure Cosmos DB for MongoDB](/azure/cosmos-db/mongodb-introduction). Microservices are typically stateless and write their state to external data stores. Azure Cosmos DB is a NoSQL database with open-source APIs for MongoDB and Cassandra.
+- **[Azure Cosmos DB](/azure/well-architected/service-guides/cosmos-db)** is a globally distributed, multiple-model database service. It stores data by using the open-source [Azure Cosmos DB for MongoDB](/azure/cosmos-db/mongodb-introduction) API. Microservices are typically stateless and write their state to external data stores. In this architecture, Azure Cosmos DB serves as the primary NoSQL database with open-source APIs for MongoDB and Cassandra where the stateless microservices write their state and application data.
 
-**[Azure Service Bus](/azure/well-architected/service-guides/service-bus/reliability)** offers reliable cloud messaging as a service and simple hybrid integration. Service Bus supports asynchronous messaging patterns that are common with microservices applications.
+- **[Azure Service Bus](/azure/well-architected/service-guides/service-bus/reliability)** is a cloud messaging service that provides asynchronous communication capabilities and hybrid integration. In this architecture, it handles asynchronous messaging between the ingestion service and workflow service.
 
-**[Azure Cache for Redis](/azure/well-architected/service-guides/azure-cache-redis/operational-excellence)** adds a caching layer to the application architecture to improve speed and performance for heavy traffic loads.
+- **[Azure Cache for Redis](/azure/well-architected/service-guides/azure-cache-redis/operational-excellence)** is an in-memory caching service based on the Redis cache. In this architecture, it improves speed and performance for heavy traffic loads by providing fast data access and reducing latency for frequently accessed data in the drone delivery system.
 
-**[Azure Monitor](/azure/azure-monitor)** collects and stores metrics and logs from the application. Use this data to monitor the application, set up alerts and dashboards, and do root cause analysis of failures. This scenario uses a Log Analytics workspace for comprehensive monitoring of the infrastructure and application.
+- **[Azure Monitor](/azure/azure-monitor)** is a comprehensive monitoring solution that collects and analyzes telemetry data. In this architecture, it collects and stores metrics and logs from all application components through a Log Analytics workspace. You can use this data to monitor the application, set up alerts and dashboards, and do root cause analysis of failures.
 
-**[Application Insights](/azure/well-architected/service-guides/application-insights)** provides extensible application performance management (APM) and monitoring for the services. Each service is instrumented with the Application Insights SDK to monitor the app and direct the data to Azure Monitor.
+- **[Application Insights](/azure/well-architected/service-guides/application-insights)** is an application performance management service that provides extensible monitoring capabilities. In this architecture, each service is instrumented with the Application Insights SDK to monitor application performance and direct the data to Azure Monitor for detailed service-level observability.
 
-**[Bicep Templates](/azure/azure-resource-manager/bicep/overview)** to configure and deploy the applications.
+- **[Bicep templates](/azure/azure-resource-manager/bicep/overview)** are infrastructure-as-code templates for deploying Azure resources. In this architecture, Bicep templates provide declarative deployment for both the Container Apps infrastructure and the drone delivery workload.
 
 ### Alternatives
 
-An alternative scenario of this example using Kubernetes is described in [Advanced Azure Kubernetes Service (AKS) microservices architecture](../../reference-architectures/containers/aks-microservices/aks-microservices-advanced.yml).
+An alternative scenario of this example that uses Kubernetes is described in [Advanced AKS microservices architecture](../../reference-architectures/containers/aks-microservices/aks-microservices-advanced.yml).
 
 ## Scenario details
 
 Your business can simplify the deployment and management of microservice containers by using Azure Container Apps. Container Apps provides a fully managed serverless environment for building and deploying modern applications.
 
 Fabrikam, Inc. (a fictional company) implements a drone delivery application where users request a drone to pick up goods for delivery. When a customer schedules a pickup, a backend system assigns a drone and notifies the user with an estimated delivery time.
 
-The microservices application was deployed to an Azure Kubernetes Service (AKS) cluster. But, the Fabrikam team wasn't taking advantage of the advanced or platform-specific AKS features. They eventually migrated the application to Azure Container Apps without much overhead. By porting their solution to Azure Container Apps, Fabrikam was able to:
+The microservices application was deployed to an AKS cluster. However, the Fabrikam team wasn't taking advantage of the advanced or platform-specific AKS features. They eventually migrated the application to Azure Container Apps without much overhead. By porting their solution to Azure Container Apps, Fabrikam was able to take the following actions:
 
 - Migrate the application nearly as-is: Very minimal code changes were required when moving their application from AKS to Azure Container Apps.
 - Deploy both infrastructure and the workload with Bicep templates: No Kubernetes YAML manifests were needed to deploy their application containers.
@@ -201,4 +201,4 @@ Principal author:
 - [Microservices architecture style](/azure/architecture/guide/architecture-styles/microservices)
 - [Design a microservices architecture](/azure/architecture/microservices/design/)
 - [CI/CD for AKS apps with Azure Pipelines](/azure/architecture/guide/aks/aks-cicd-azure-pipelines)
-- [Advanced Azure Kubernetes Service (AKS) microservices architecture](/azure/architecture/reference-architectures/containers/aks-microservices/aks-microservices-advanced)
+- [Advanced AKS microservices architecture](/azure/architecture/reference-architectures/containers/aks-microservices/aks-microservices-advanced)

docs/example-scenario/serverless/microservices-with-container-apps.yml

@@ -14,6 +14,7 @@ metadata:
     - example-scenario
     - arb-web
     - arb-containers
+  ai-usage: ai-assisted
 name: Deploy microservices with Azure Container Apps
 azureCategories:
   - developer-tools