Merge pull request #467 from MicrosoftDocs/master

8/16/2019 AM Publish

docs/cloud-adoption/getting-started/what-is-azure.md

@@ -31,7 +31,7 @@ Within each rack or cluster, most of the servers are designated to run these vir
 
 Each instance of the fabric controller is connected to another set of servers running cloud orchestration software, typically known as a **front end**. The front end hosts the web services, RESTful APIs, and internal Azure databases used for all functions the cloud performs.
 
-For example, the front end hosts the services that handle customer requests to allocate Azure resources such as [virtual networks](/azure/virtual-network/virtual-networks-overview), [virtual machines](/azure/virtual-machines), and services like [Cosmos DB](/azure/cosmos-db/introduction). First, the front end validates the user and verifies the user is authorized to allocate the requested resources. If so, the front end checks a database to locate a server rack with sufficient capacity and then instructs the fabric controller on that rack to allocate the resource.
+For example, the front end hosts the services that handle customer requests to allocate Azure resources such as [virtual machines](/azure/virtual-machines), and services like [Cosmos DB](/azure/cosmos-db/introduction). First, the front end validates the user and verifies the user is authorized to allocate the requested resources. If so, the front end checks a database to locate a server rack with sufficient capacity and then instructs the fabric controller on that rack to allocate the resource.
 
 So fundamentally, Azure is a huge collection of servers and networking hardware running a complex set of distributed applications to orchestrate the configuration and operation of the virtualized hardware and software on those servers. It is this orchestration that makes Azure so powerful—users are no longer responsible for maintaining and upgrading hardware because Azure does all this behind the scenes.
 

docs/data-guide/relational-data/data-warehousing.md

@@ -72,7 +72,7 @@ As a general rule, SMP-based warehouses are best suited for small to medium data
 
 Beyond data sizes, the type of workload pattern is likely to be a greater determining factor. For example, complex queries may be too slow for an SMP solution, and require an MPP solution instead. MPP-based systems usually have a performance penalty with small data sizes, because of how jobs are distributed and consolidated across nodes. If your data sizes already exceed 1 TB and are expected to continually grow, consider selecting an MPP solution. However, if your data sizes are smaller, but your workloads are exceeding the available resources of your SMP solution, then MPP may be your best option as well.
 
-The data accessed or stored by your data warehouse could come from a number of data sources, including a data lake, such as [Azure Data Lake Store](/azure/data-lake-store/). For a video session that compares the different strengths of MPP services that can use Azure Data Lake, see [Azure Data Lake and Azure Data Warehouse: Applying Modern Practices to Your App](https://azure.microsoft.com/resources/videos/build-2016-azure-data-lake-and-azure-data-warehouse-applying-modern-practices-to-your-app/).
+The data accessed or stored by your data warehouse could come from a number of data sources, including a data lake, such as [Azure Data Lake Storage](/azure/data-lake-store/). For a video session that compares the different strengths of MPP services that can use Azure Data Lake, see [Azure Data Lake and Azure Data Warehouse: Applying Modern Practices to Your App](https://azure.microsoft.com/resources/videos/build-2016-azure-data-lake-and-azure-data-warehouse-applying-modern-practices-to-your-app/).
 
 SMP systems are characterized by a single instance of a relational database management system sharing all resources (CPU/Memory/Disk). You can scale up an SMP system. For SQL Server running on a VM, you can scale up the VM size. For Azure SQL Database, you can scale up by selecting a different service tier.
 
@@ -147,7 +147,7 @@ The following tables summarize the key differences in capabilities.
 
 [3] With SQL Data Warehouse, you can restore a database to any available restore point within the last seven days. Snapshots start every four to eight hours and are available for seven days. When a snapshot is older than seven days, it expires and its restore point is no longer available.
 
-[4] Consider using an [external Hive metastore](/azure/hdinsight/hdinsight-hadoop-provision-linux-clusters#use-hiveoozie-metastore) that can be backed up and restored as needed. Standard backup and restore options that apply to Blob Storage or Data Lake Store can be used for the data, or third-party HDInsight backup and restore solutions, such as [Imanis Data](https://azure.microsoft.com/blog/imanis-data-cloud-migration-backup-for-your-big-data-applications-on-azure-hdinsight/) can be used for greater flexibility and ease of use.
+[4] Consider using an [external Hive metastore](/azure/hdinsight/hdinsight-hadoop-provision-linux-clusters#use-hiveoozie-metastore) that can be backed up and restored as needed. Standard backup and restore options that apply to Blob Storage or Data Lake Storage can be used for the data, or third-party HDInsight backup and restore solutions, such as [Imanis Data](https://azure.microsoft.com/blog/imanis-data-cloud-migration-backup-for-your-big-data-applications-on-azure-hdinsight/) can be used for greater flexibility and ease of use.
 
 ### Scalability capabilities
 

docs/reference-architectures/ai/mlops-python.md

@@ -146,4 +146,4 @@ The retraining pipeline also requires a form of compute. This architecture uses
 
 To deploy this reference architecture, follow the steps described in the [GitHub repo][repo].
 
-[repo]: https://github.com/Microsoft/MLOpsPython'
+[repo]: https://github.com/Microsoft/MLOpsPython

docs/reference-architectures/hybrid-networking/hub-spoke.md

@@ -109,7 +109,7 @@ The deployment creates the following resource groups in your subscription:
 - onprem-jb-rg
 - onprem-vnet-rg
 - spoke1-vnet-rg
-- spoke2-vent-rg
+- spoke2-vnet-rg
 
 The template parameter files refer to these names, so if you change them, update the parameter files to match.
 

docs/topics/high-performance-computing.md

@@ -2,7 +2,7 @@
 title: High Performance Computing (HPC) on Azure
 description: A guide to building running HPC workloads on Azure
 author: adamboeglin
-ms.date: 2/4/2019
+ms.date: 8/14/2019
 ---
 <!-- markdownlint-disable MD033 -->
 <!-- markdownlint-disable MD026 -->
@@ -271,8 +271,7 @@ The following are examples of cluster and workload managers that can run in Azur
 - [TIBCO DataSynapse GridServer](https://azure.microsoft.com/blog/tibco-datasynapse-comes-to-the-azure-marketplace/)
 - [Bright Cluster Manager](http://www.brightcomputing.com/technology-partners/microsoft)
 - [IBM Spectrum Symphony and Symphony LSF](https://azure.microsoft.com/blog/ibm-and-microsoft-azure-support-spectrum-symphony-and-spectrum-lsf/)
-- [PBS Pro](http://pbspro.org)
-- [Altair](http://www.altair.com/)
+- [Altair PBS Works](https://web.altair.com/pbs-on-azure)
 - [Rescale](https://www.rescale.com/azure/)
 - [Microsoft HPC Pack](https://technet.microsoft.com/library/mt744885.aspx)
   - [HPC Pack for Windows](/azure/virtual-machines/windows/hpcpack-cluster-options)