From df430d25ad5179be90794059002fe2ea9594a7b9 Mon Sep 17 00:00:00 2001 From: AzureMentor <30055505+AzureMentor@users.noreply.github.com> Date: Wed, 14 Aug 2019 16:34:59 -0400 Subject: [PATCH 1/5] It is now known as "Azure Data Lake Storage" (#1698) The current name is "Azure Data Lake Storage". "Azure Data Lake Store" was the previous name. --- docs/data-guide/relational-data/data-warehousing.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/data-guide/relational-data/data-warehousing.md b/docs/data-guide/relational-data/data-warehousing.md index 72b367f1bee..d21c075f253 100644 --- a/docs/data-guide/relational-data/data-warehousing.md +++ b/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 From c440a199c91d5b770ee44dca9c749eea5a4173d0 Mon Sep 17 00:00:00 2001 From: JanetCThomas <49959026+JanetCThomas@users.noreply.github.com> Date: Wed, 14 Aug 2019 13:38:01 -0700 Subject: [PATCH 2/5] fix duplicate links (#1694) I suggest removing the virtual network link unless you can point it to a different URL. Currently it redirects to the virtual machine page, which is the same as the link that follows in the sentence. --- docs/cloud-adoption/getting-started/what-is-azure.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/cloud-adoption/getting-started/what-is-azure.md b/docs/cloud-adoption/getting-started/what-is-azure.md index b6b1b34188f..bd338394563 100644 --- a/docs/cloud-adoption/getting-started/what-is-azure.md +++ b/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. From 4db2988c8343dcdd037eeb5be1b0ba8e71d70d01 Mon Sep 17 00:00:00 2001 From: Sushant Divate Date: Wed, 14 Aug 2019 15:08:22 -0700 Subject: [PATCH 3/5] Fix links to GitHub Repo for Implementation (#1706) * Fix links to GitHub Repo for Implementation Fix links to GitHub Repo pointing to implementation (https://github.com/microsoft/MLOpsPython) --- docs/reference-architectures/ai/mlops-python.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/reference-architectures/ai/mlops-python.md b/docs/reference-architectures/ai/mlops-python.md index 1a0be290698..eb087dc9147 100644 --- a/docs/reference-architectures/ai/mlops-python.md +++ b/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 From 0862d786dda6113a7e6e916b8130c109ebc49c22 Mon Sep 17 00:00:00 2001 From: Derek Date: Wed, 14 Aug 2019 19:32:03 -0500 Subject: [PATCH 4/5] Move Altair and PBSPro to the same line (#1708) * Move Altair and PBSPro to the same line Same product, different licensing. Closes https://github.com/MicrosoftDocs/architecture-center/issues/1695 * Update per Adam --- docs/topics/high-performance-computing.md | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/docs/topics/high-performance-computing.md b/docs/topics/high-performance-computing.md index 10b12b5f3b2..6d544c03722 100644 --- a/docs/topics/high-performance-computing.md +++ b/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 --- @@ -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) From 3957e157231dda9c7382b58c346e64d700491711 Mon Sep 17 00:00:00 2001 From: jpalo Date: Thu, 15 Aug 2019 14:02:45 +0300 Subject: [PATCH 5/5] Update hub-spoke.md Typo correction --- docs/reference-architectures/hybrid-networking/hub-spoke.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/reference-architectures/hybrid-networking/hub-spoke.md b/docs/reference-architectures/hybrid-networking/hub-spoke.md index 6ac63736f39..9272ceb575d 100644 --- a/docs/reference-architectures/hybrid-networking/hub-spoke.md +++ b/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.