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|Azure Arc Extension for Azure Data Studio<br/>`arc`<br/>`azcli`|<br/>1.8.0 ([Download](https://aka.ms/ads-arcdata-ext))</br>1.8.0 ([Download](https://aka.ms/ads-azcli-ext))|
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|SQL Database version | 964 |
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|SQL Database version |968|
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### Release notes
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#### Arc-enabled SQL Server
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Arc SQL Server | Backups | Configure backups at DB level for Arc enabled SQL Server in Portal and CLI | Public Preview
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Arc SQL Server | Support for moving Arc-enabled SQL Server instances and child resources (DBs, AGs) across different subscriptions & resource groups | Public Preview
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Arc SQL Server | Set feature flags via the Azure CLI | GA
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Arc SQL Server | Billing | Support unlimited virtualization benefit for PAYG and Software Assurance customers | GA
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-->
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Arc SQL Server | Allow the database inventory feature for all license types | GA
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title: What is Azure Arc-enabled VMware vSphere?
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description: Azure Arc-enabled VMware vSphere extends Azure governance and management capabilities to VMware vSphere infrastructure and delivers a consistent management experience across both platforms.
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ms.topic: overview
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ms.date: 03/13/2024
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ms.date: 03/21/2024
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ms.custom: references_regions
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ms.service: azure-arc
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ms.subservice: azure-arc-vmware-vsphere
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You have the flexibility to start with either option, and incorporate the other one later without any disruption. With both the options, you enjoy the same consistent experience.
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## Supported VMware vSphere versions
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Azure Arc-enabled VMware vSphere currently works with vCenter Server versions 7 and 8.
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For the most up-to-date information about region availability of Azure Arc-enabled VMware vSphere, see [Azure Products by Region](https://azure.microsoft.com/explore/global-infrastructure/products-by-region/?products=azure-arc®ions=all) page.
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## Data Residency
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Azure Arc-enabled VMware vSphere doesn't store/process customer data outside the region the customer deploys the service instance in.
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## Azure Kubernetes Service (AKS) Arc on VMware (preview)
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Starting in March 2024, Azure Kubernetes Service (AKS) enabled by Azure Arc on VMware is available for preview. AKS Arc on VMware enables you to use Azure Arc to create new Kubernetes clusters on VMware vSphere. For more information, see [What is AKS enabled by Arc on VMware?](/azure/aks/hybrid/aks-vmware-overview).
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The following capabilities are available in the AKS Arc on VMware preview:
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-**Simplified infrastructure deployment on Arc-enabled VMware vSphere**: Onboard VMware vSphere to Azure using a single-step process with the AKS Arc extension installed.
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-**Azure CLI**: A consistent command-line experience, with [AKS Arc on Azure Stack HCI 23H2](/azure/aks/hybrid/aks-create-clusters-cli), for creating and managing Kubernetes clusters. Note that the preview only supports a limited set commands.
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-**Cloud-based management**: Use familiar tools such as Azure CLI to create and manage Kubernetes clusters on VMware.
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-**Support for managing and scaling node pools and clusters**.
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## Next steps
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- Plan your resource bridge deployment by reviewing the [support matrix for Arc-enabled VMware vSphere](support-matrix-for-arc-enabled-vmware-vsphere.md).
Copy file name to clipboardExpand all lines: articles/azure-netapp-files/use-availability-zones.md
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ms.date: 11/17/2022
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ms.author: anfdocs
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---
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# Use availability zones for high availability in Azure NetApp Files (preview)
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# Use availability zones for high availability in Azure NetApp Files
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Azure [availability zones](../availability-zones/az-overview.md#availability-zones) are physically separate locations within each supporting Azure region that are tolerant to local failures. Failures can range from software and hardware failures to events such as earthquakes, floods, and fires. Tolerance to failures is achieved because of redundancy and logical isolation of Azure services. To ensure resiliency, a minimum of three separate availability zones are present in all [availability zone-enabled regions](../availability-zones/az-overview.md#azure-regions-with-availability-zones).
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## March 2024
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*[Availability zone volume placement](manage-availability-zone-volume-placement.md) is now generally available (GA).
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You can deploy new volumes in the logical availability zone of your choice to create cross-zone volumes to improve resiliency in case of zonal failures. This feature is available in all availability zone-enabled regions with Azure NetApp Files presence.
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The [populate existing volume](manage-availability-zone-volume-placement.md#populate-an-existing-volume-with-availability-zone-information) feature is still in preview.
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*[Capacity pool enhancement](azure-netapp-files-set-up-capacity-pool.md): The 1 TiB capacity pool feature is now generally available (GA).
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The 1 TiB lower limit for capacity pools using Standard network features is now generally available (GA). You still must register the feature.
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|| Description |
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| --- | --- |
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|**[Azure Cosmos DB for Mongo DB vCore](#implement-vector-database-functionalities-using-our-api-for-mongodb-vcore)**| Store your application data and vector embeddings together in a single MongoDB-compatible service featuring native support for vector search. |
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|**[Azure Cosmos DB for PostgreSQL](#implement-vector-database-functionalities-using-our-api-for-postgresql)**| Store your data and vectors together in a scalable PostgreSQL offering with native support for vector search. |
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|**[Azure Cosmos DB for Mongo DB vCore](#implement-vector-database-functionalities-using-our-api-for-mongodb-vcore)**| Store your application data and vector embeddings together in a single MongoDB-compatible service featuring natively integrated vector database. |
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|**[Azure Cosmos DB for PostgreSQL](#implement-vector-database-functionalities-using-our-api-for-postgresql)**| Store your data and vectors together in a scalable PostgreSQL offering with natively integrated vector database. |
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|**[Azure Cosmos DB for NoSQL with Azure AI Search](#implement-vector-database-functionalities-using-our-nosql-api-and-ai-search)**| Augment your Azure Cosmos DB data with semantic and vector search capabilities of Azure AI Search. |
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## What is a vector database?
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It's increasingly popular to use the [vector search](#vector-search) feature in a vector database to enable [retrieval-augmented generation](#retrieval-augmented-generation) that harnesses LLMs and custom data or domain-specific information. This process involves extracting pertinent information from a custom data source and integrating it into the model request through prompt engineering.
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A robust mechanism is necessary to identify the most relevant data from the custom source that can be passed to the LLM. Our vector search features convert the data in your database into embeddings and store them as vectors for future use. The vector search feature captures the semantic meaning of the text and going beyond mere keywords to comprehend the context. Moreover, this mechanism allows you to optimize for the LLM’s limit on the number of [tokens](#tokens) per request.
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A robust mechanism is necessary to identify the most relevant data from the custom source that can be passed to the LLM. Our integrated vector database converts the data in your database into embeddings and store them as vectors for future use. The vector search captures the semantic meaning of the text and going beyond mere keywords to comprehend the context. Moreover, this mechanism allows you to optimize for the LLM’s limit on the number of [tokens](#tokens) per request.
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Prior to sending a request to the LLM, the user input/query/request is also transformed into an embedding, and vector search techniques are employed to locate the most similar embeddings within the database. This technique enables the identification of the most relevant data records in the database. These retrieved records are then supplied as input to the LLM request using [prompt engineering](#prompts-and-prompt-engineering).
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Here are multiple ways to implement RAG on your data by using our vector database functionalities.
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## Implement vector database functionalities using our API for MongoDB vCore
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Use the native vector search feature in [Azure Cosmos DB for MongoDB vCore](mongodb/vcore/vector-search.md), which offers an efficient way to store, index, and search high-dimensional vector data directly alongside other application data. This approach removes the necessity of migrating your data to costlier alternative vector databases and provides a seamless integration of your AI-driven applications.
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Use the natively integrated vector database in [Azure Cosmos DB for MongoDB vCore](mongodb/vcore/vector-search.md), which offers an efficient way to store, index, and search high-dimensional vector data directly alongside other application data. This approach removes the necessity of migrating your data to costlier alternative vector databases and provides a seamless integration of your AI-driven applications.
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### Vector database implementation code samples
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## Implement vector database functionalities using our API for PostgreSQL
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Use the native vector search feature in [Azure Cosmos DB for PostgreSQL](postgresql/howto-use-pgvector.md), which offers an efficient way to store, index, and search high-dimensional vector data directly alongside other application data. This approach removes the necessity of migrating your data to costlier alternative vector databases and provides a seamless integration of your AI-driven applications.
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Use the natively integrated vector database in [Azure Cosmos DB for PostgreSQL](postgresql/howto-use-pgvector.md), which offers an efficient way to store, index, and search high-dimensional vector data directly alongside other application data. This approach removes the necessity of migrating your data to costlier alternative vector databases and provides a seamless integration of your AI-driven applications.
## Implement vector database functionalities using our NoSQL API and AI Search
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The native vector search feature in our NoSQL API is under development. In the meantime, you may implement RAG patterns with Azure Cosmos DB for NoSQL and [Azure AI Search](../search/vector-search-overview.md). This approach enables powerful integration of your data residing in the NoSQL API into your AI-oriented applications.
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The natively integrated vector database in our NoSQL API will become available in mid-2024. In the meantime, you may implement RAG patterns with Azure Cosmos DB for NoSQL and [Azure AI Search](../search/vector-search-overview.md). This approach enables powerful integration of your data residing in the NoSQL API into your AI-oriented applications.
Copy file name to clipboardExpand all lines: articles/ddos-protection/ddos-protection-reference-architectures.md
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author: AbdullahBell
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ms.service: ddos-protection
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ms.topic: conceptual
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ms.date: 06/15/2023
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ms.date: 03/27/2024
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ms.author: abell
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In this architecture diagram Azure DDoS IP Protection is enabled on the public IP Address.
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> [!NOTE]
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> Azure DDoS Protection protects the Public IPs of Azure resource. DDoS infrastructure protection, which requires no configuration and is enabled by default, only protects the Azure underlying platform infrastructure (e.g. Azure DNS). For more information, see [Azure DDoS Protection overview](ddos-protection-overview.md).
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> At no additional cost, Azure DDoS infrastructure protection protects every Azure service that uses public IPv4 and IPv6 addresses. This DDoS protection service helps to protect all Azure services, including platform as a service (PaaS) services such as Azure DNS. For more information, see [Azure DDoS Protection overview](ddos-protection-overview.md).
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For more information about hub-and-spoke topology, see [Hub-spoke network topology](/azure/architecture/reference-architectures/hybrid-networking/hub-spoke?tabs=cli).
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ms.date: 03/27/2024
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- PaaS services (multi-tenant), which includes Azure App Service Environment for Power Apps, Azure API Management in deployment modes other than APIM with virtual network integration (For more information see https://techcommunity.microsoft.com/t5/azure-network-security-blog/azure-ddos-standard-protection-now-supports-apim-in-vnet/ba-p/3641671), and Azure Virtual WAN aren't currently supported.
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- Protecting a public IP resource attached to a NAT Gateway isn't supported.
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- Virtual machines in Classic/RDFE deployments aren't supported.
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- VPN gateway or Virtual network gateway is protected by a fixed DDoS policy. Adaptive tuning isn't supported at this stage.
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- VPN gateway or Virtual network gateway is protected by a DDoS policy. Adaptive tuning isn't supported at this stage.
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- Partially supported: the Azure DDoS Protection service can protect a public load balancer with a public IP address prefix linked to its frontend. It effectively detects and mitigates DDoS attacks. However, telemetry and logging for the protected public IP addresses within the prefix range are currently unavailable.
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Users working in hybrid environments might be managing OT alerts in [Defender for IoT](https://portal.azure.com/#view/Microsoft_Azure_IoT_Defender/IoTDefenderDashboard/~/Getting_started) on the Azure portal, the OT sensor, and an on-premises management console.
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Alert statuses are fully synchronized between the Azure portal and the OT sensor, and between the sensor and the on-premises management console. This means that regardless of where you manage the alert in Defender for IoT, the alert is updated in other locations as well.
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> [!NOTE]
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> While the sensor console displays an alert's **Last detection** field in real-time, Defender for IoT in the Azure portal may take up to one hour to display the updated time. This explains a scenario where the last detection time in the sensor console isn't the same as the last detection time in the Azure portal.
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Alert statuses are otherwise fully synchronized between the Azure portal and the OT sensor, and between the sensor and the on-premises management console. This means that regardless of where you manage the alert in Defender for IoT, the alert is updated in other locations as well.
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Setting an alert status to **Closed** or **Muted** on a sensor or on-premises management console updates the alert status to **Closed** on the Azure portal. On the on-premises management console, the **Closed** alert status is called **Acknowledged**.
Copy file name to clipboardExpand all lines: articles/deployment-environments/quickstart-create-dev-center-project-azure-resource-manager.md
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# Customer intent: As an enterprise admin, I want a quick method to create and configure a Dev Center and Project resource to evaluate Deployment Environments.
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# Quickstart: Create dev center and project for Azure Deployment Environments by using ARM template
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# Quickstart: Create dev center and project for Azure Deployment Environments by using an ARM template
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This quickstart describes how to use an Azure Resource Manager template (ARM template) to create and configure a dev center and project for creating an environment.
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