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Copy file name to clipboardExpand all lines: articles/api-management/api-management-security-controls.md
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title: Security controls for Azure API Management
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description: A checklist of security controls for evaluating API Management
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services: api-management
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author: msmbaldwin
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manager: rkarlin
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author: vladvino
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ms.service: api-management
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ms.topic: conceptual
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ms.date: 09/04/2019
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ms.author: mbaldwin
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ms.date: 09/23/2019
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ms.author: vlvinogr
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---
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# Security controls for API Management
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## Network
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| Security control | Yes/No | Notes |
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|---|---|--|
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| Service endpoint support| No ||
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| VNet injection support| Yes ||
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| Network isolation and firewalling support| Yes | Using networking security groups (NSG) and Azure Application Gateway (or other software appliance) respectively. |
| Security control | Yes/No | Notes | Documentation |
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|---|---|--|--|
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| Service endpoint support| No |||
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| VNet injection support| Yes |||
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| Network isolation and firewalling support| Yes | Using networking security groups (NSG) and Azure Application Gateway (or other software appliance) respectively. ||
| Control and management plane logging and audit| Yes |[Azure Monitor activity logs](../azure-monitor/platform/activity-logs-overview.md)||
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| Data plane logging and audit| Yes |[Azure Monitor diagnostic logs](../azure-monitor/platform/resource-logs-overview.md) and (optionally) [Azure Application Insights](../azure-monitor/app/app-insights-overview.md). ||
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## Identity
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| Security control | Yes/No | Notes|
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|---|---|--|
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| Authentication| Yes ||
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| Authorization| Yes ||
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| Security control | Yes/No | Notes| Documentation |
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|---|---|--|--|
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| Authentication| Yes |||
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| Authorization| Yes |||
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## Data protection
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| Security control | Yes/No | Notes |
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|---|---|--|
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| Server-side encryption at rest: Microsoft-managed keys | Yes | Sensitive data such as certificates, keys, and secret-named values are encrypted with service-managed, per service instance keys. |
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| Server-side encryption at rest: customer-managed keys (BYOK) | No | All encryption keys are per service instance and are service managed. |
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| Column level encryption (Azure Data Services)| N/A ||
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| Encryption in transit (such as ExpressRoute encryption, in VNet encryption, and VNet-VNet encryption)| Yes |[Express Route](../expressroute/index.yml) and VNet encryption is provided by [Azure networking](../virtual-network/index.yml). |
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| API calls encrypted| Yes | Management plane calls are made through [Azure Resource Manager](../azure-resource-manager/index.yml) over TLS. A valid JSON web token (JWT) is required. Data plane calls can be secured with TLS and one of supported authentication mechanisms (for example, client certificate or JWT).
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| Security control | Yes/No | Notes | Documentation |
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|---|---|--|--|
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| Server-side encryption at rest: Microsoft-managed keys | Yes | Sensitive data such as certificates, keys, and secret-named values are encrypted with service-managed, per service instance keys. ||
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| Server-side encryption at rest: customer-managed keys (BYOK) | No | All encryption keys are per service instance and are service managed. ||
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| Column level encryption (Azure Data Services)| N/A |||
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| Encryption in transit (such as ExpressRoute encryption, in VNet encryption, and VNet-VNet encryption)| Yes |[Express Route](../expressroute/index.yml) and VNet encryption is provided by [Azure networking](../virtual-network/index.yml). ||
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| API calls encrypted| Yes | Management plane calls are made through [Azure Resource Manager](../azure-resource-manager/index.yml) over TLS. A valid JSON web token (JWT) is required. Data plane calls can be secured with TLS and one of supported authentication mechanisms (for example, client certificate or JWT).||
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|
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## Configuration management
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| Security control | Yes/No | Notes|
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|---|---|--|
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| Configuration management support (versioning of configuration, etc.)| Yes | Using the [Azure API Management DevOps Resource Kit](https://aka.ms/apimdevops)|
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| Security control | Yes/No | Notes| Documentation |
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|---|---|--|--|
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| Configuration management support (versioning of configuration, etc.)| Yes | Using the [Azure API Management DevOps Resource Kit](https://aka.ms/apimdevops)||
Copy file name to clipboardExpand all lines: articles/hpc-cache/hpc-cache-overview.md
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@@ -4,13 +4,13 @@ description: Describes Azure HPC Cache, a file access accelerator solution for h
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author: ekpgh
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ms.service: hpc-cache
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ms.topic: overview
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ms.date: 09/19/2019
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ms.date: 09/24/2019
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ms.author: v-erkell
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---
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# What is Azure HPC Cache? (Preview)
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Azure HPC Cache speeds access to your data for high-performance computing (HPC) tasks. By caching files in Azure, it makes the scalability of cloud computing available even for workflows where your data is stored across WAN links, such as in your local datacenter network-attached storage (NAS) environment.
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Azure HPC Cache speeds access to your data for high-performance computing (HPC) tasks. By caching files in Azure, Azure HPC Cache brings the scalability of cloud computing to your existing workflow. This service can be used even for workflows where your data is stored across WAN links, such as in your local datacenter network-attached storage (NAS) environment.
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Azure HPC Cache is easy to launch and monitor from the Azure portal. Existing NFS storage or new Blob containers can become part of its aggregated namespace, which makes client access simple even if you change the back-end storage target.
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Many life sciences workflows can benefit from scale-out file caching.
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A research institute that wants to port its genomic analysis workflows into Azure can easily shift them by using Azure HPC Cache. Because the cache provides POSIX file access, they can run their existing client-side workflow in the cloud without any changes.
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A research institute that wants to port its genomic analysis workflows into Azure can easily shift them by using Azure HPC Cache. Because the cache provides POSIX file access, no client-side changes are needed to run their existing client workflow in the cloud.
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Azure HPC Cache also can be leveraged to improve efficiency in tasks like secondary analysis, pharmacological simulation, or AI-driven image analysis.
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### Financial services analytics
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An Azure HPC Cache can help speed up quantitative analysis calculations, risk analysis workloads, and Monte Carlo simulations to give financial services companies better insight to make strategic decisions.
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An Azure HPC Cache deployment can help speed up quantitative analysis calculations, risk analysis workloads, and Monte Carlo simulations to give financial services companies better insight to make strategic decisions.
The Azure HPC Cache public preview is restricted to ensure service quality. Request access by filling out [this form](https://aka.ms/onboard-hpc-cache). After your subscription is added to the access list you can create test caches.
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The Azure HPC Cache public preview is restricted to ensure service quality. Request access by filling out [this form](https://aka.ms/onboard-hpc-cache). After your subscription is added to the access list, you can create test caches.
Copy file name to clipboardExpand all lines: articles/search/search-howto-large-index.md
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## Option 1: Pass multiple documents
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One of the simplest mechanisms for indexing a larger data set is to submit multiple documents or records in a single request. As long as the entire payload is under 16 MB, a request can handle up to 1000 documents in a bulk upload operation. These limits apply whether you are using the [Add Documents (REST)](https://docs.microsoft.com/rest/api/searchservice/addupdate-or-delete-documents) or [Index class](https://docs.microsoft.com/dotnet/api/microsoft.azure.search.models.index?view=azure-dotnet) in the .NET SDK. For either API, you would package 1000 documents in the body of each request.
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One of the simplest mechanisms for indexing a larger data set is to submit multiple documents or records in a single request. As long as the entire payload is under 16 MB, a request can handle up to 1000 documents in a bulk upload operation. These limits apply whether you are using the [Add Documents REST API](https://docs.microsoft.com/rest/api/searchservice/addupdate-or-delete-documents) or the [Index method](https://docs.microsoft.com/dotnet/api/microsoft.azure.search.documentsoperationsextensions.index?view=azure-dotnet) in the .NET SDK. For either API, you would package 1000 documents in the body of each request.
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Batch indexing is implemented for individual requests using REST or .NET, or through indexers. A few indexers operate under different limits. Specifically, Azure Blob indexing sets batch size at 10 documents in recognition of the larger average document size. For indexers based on the [Create Indexer (REST)](https://docs.microsoft.com/rest/api/searchservice/Create-Indexer), you can set the `BatchSize` argument to customize this setting to better match the characteristics of your data.
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Batch indexing is implemented for individual requests using REST or .NET, or through indexers. A few indexers operate under different limits. Specifically, Azure Blob indexing sets batch size at 10 documents in recognition of the larger average document size. For indexers based on the [Create Indexer REST API](https://docs.microsoft.com/rest/api/searchservice/Create-Indexer), you can set the `BatchSize` argument to customize this setting to better match the characteristics of your data.
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> [!NOTE]
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> To keep document size down, avoid adding non-queryable data to an index. Images and other binary data are not directly searchable and shouldn't be stored in the index. To integrate non-queryable data into search results, you should define a non-searchable field that stores a URL reference to the resource.
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+ Schedulers allow you to parcel out indexing at regular intervals so that you can spread it out over time.
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+ Scheduled indexing can resume at the last known stopping point. If a data source is not fully crawled within a 24-hour window, the indexer will resume indexing on day two at wherever it left off.
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+ Partitioning data into smaller individual data sources enables parallel processing. You can break a large data set into smaller data sets on your source data platform (such as Azure Blob storage or Azure SQL Database), and then create multiple [data source objects](https://docs.microsoft.com/rest/api/searchservice/create-data-source)on Azure Search that can be indexed in parallel.
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+ Partitioning data into smaller individual data sources enables parallel processing. You can break up source data into smaller components, such as into multiple containers in Azure Blob storage, and then create corresponding, multiple [data source objects](https://docs.microsoft.com/rest/api/searchservice/create-data-source)in Azure Search that can be indexed in parallel.
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> [!NOTE]
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> Indexers are data-source-specific, so using an indexer approach is only viable for selected data sources on Azure: [SQL Database](search-howto-connecting-azure-sql-database-to-azure-search-using-indexers.md), [Blob storage](search-howto-indexing-azure-blob-storage.md), [Table storage](search-howto-indexing-azure-tables.md), [Cosmos DB](search-howto-index-cosmosdb.md).
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