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Join us March 15th for the first Azure Blockchain AMA

Join us Wednesday, March 15th at 9am PST/12pm EST for the first Azure Blockchain-hosted Ask Me Anything (AMA) on Microsoft Tech Community. We receive great feedback and input about blockchain through customer engagements and other channels, but we haven’t interacted broadly in real-time, until now. You’ll be able to connect directly with the Blockchain team, who will be on hand to answer your questions and listen to feedback.

Add the AMA to your calendar!

When:

Wednesday, March 15, 2017 from 09:00 am to 10:00 am Pacific Time

Where:

The Azure Blockchain Community

What’s an AMA session?

We’ll have folks from the Azure Blockchain engineering team available to answer any questions you have. You can ask us anything about our products, services, or even our team!

Why are we doing an AMA?

Connect directly with customers and hear your feedback and answer your questions, such as:

What is Microsoft’s strategy around blockchain?
Why have blockchain on Azure?
What are the most common blockchain scenarios for industry X or vertical Y?
How do I submit blockchain feature requests?

Who will be at the AMA?

We’ll have PMs, developers, and technical thought leaders from the Azure Blockchain engineering team.

We’re looking forward to this AMA and to connect with you directly.
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Azure Data Lake Analytics and Data Lake Store now available in Europe

Azure Data Lake Analytics and Azure Data Lake Store are now available in the North Europe region. For more information about pricing, please visit the Data Lake Analytics Pricing and Data Lake Store Pricing webpages. Data Lake Analytics is a cloud analytics service for developing and running massively parallel data transformation and processing programs in U-SQL, R, Python, and .NET over petabytes of data. Data Lake Store is a no-limit cloud data lake built so enterprises can unlock value from unstructured, semi-structured, and structured data. To learn more about these services, please visit the Data Lake Analytics and Data Lake Store webpages.
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Leverage the Azure CLI with these examples

Customer response to our CLI has been great since the preview release last September and the GA announcement in February. This has been a great opportunity for us to work with customers and learn what is working well and what is still needed. Some of the feedback we’ve received is that we need to provide more documentation and examples to fully leverage all the new features.

Based on this feedback, we’re delivering samples for Linux and Windows VMs, Web Apps, and Azure SQL Database, all of which can be found on this overview page. For Linux VMs, we’re highlighting best practices for creating, monitoring, and troubleshooting, with similar scripts for Windows VMs. For Microsoft SQL Azure, we get you started with scripts for creating single and pooled databases. For Web Apps, we show you how to create a Web App with integration to your favorite deployment method (git, GitHub, Docker, etc.) as well as configure, scale, connect to resources, and monitor your Web Apps, all from the command line. Here’s an example that creates an Azure Web App that is ready to deploy code from GitHub:

All of the example scripts can be used in the CLI “as is”, and also as documentation to help you understand how to develop your own scripts.

You can also get started with the CLI using the rest of our updated docs and samples, including installing and updating the CLI, working with Virtual Machines, creating a complete Linux environment including VMs, Scale Sets, Storage, and network. The Azure CLI is open source and on GitHub.

We’re continuing to provide updates based on your ongoing feedback, so please share any suggestions you may have. Reach out with suggestions or questions via StackOverflow using the azure-cli tag, or email us directly at azfeedback@microsoft.com.
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Portal Preview of Azure Resource Policy

Since the first release of resource policies last April, we have received valuable feedback from customers and with this feedback we have added new features. I’m pleased to announce the following new features for Azure Resource Policies:

Policy management in portal (preview)
Policy with parameters

Policy Management in Portal

Many customers requested the ability to manage policies through the Azure portal. Using the portal reduces the learning curve for creating policies and makes managing the policies easier. It is now available in Azure preview portal.

Similar to working with Identity and Access Control, you can configure resource policies for subscriptions and resource groups from the settings menu. You can view what policies are assigned to the current subscriptions and resource groups, and add new policy assignments. For common policies, you can use the built-in policies and customize the values you need. For example, when creating a geo-compliance policy, the UI simply asks you for a list of permitted locations. You can provide the name and a description that are seen by users when they violate the policy.

 

Figure 1: View all policy assignments

 

Figure 2: Adding new policy assignment

Policy using Parameters

With API version 2016-12-01, you can add parameters to your policy template. The parameters enable you to customize the policy definition. The preceding example for the portal utilizes parameters in the policy. There are two benefits:

Reduce the number of policy definitions to manage. For example, you previously needed multiple policies to manage tags for different applications in different resource groups. Now, you can consolidate them into one policy definition with tag name as a parameter. You provide the value of the tag name when you assign the policy to the application.
Separate access control for policy definition and policy management. Previously, if you used resource groups as the scope for most of your policy assignments, all users who assigned a policy to a resource groups also needed permission to create policy definitions. This permission was required because different assignments required different policy definitions. However, granting this permission created the risk that they could potentially modify other policy definitions. By using parameters, users no longer need to create their own policy definitions.

{
"properties": {
"displayName": "Allowed virtual machine SKUs",
"policyType": "BuiltIn",
"description": "This policy enables you to specify a set of virtual machine SKUs that your organization can deploy.",
"parameters": {
"listOfAllowedSKUs": {
"type": "Array",
"metadata": {
"description": "The list of SKUs that can be specified for virtual machines.",
"displayName": "Allowed SKUs",
"strongType": "VMSKUs"
}
}
},
"policyRule": {
"if": {
"allOf": [
{
"field": "type",
"equals": "Microsoft.Compute/virtualMachines"
},
{
"not": {
"field": "Microsoft.Compute/virtualMachines/sku.name",
"in": "[parameters('listOfAllowedSKUs&039;)]"
}
}
]
},
"then": { "effect": "Deny" }
}
},
"id": "/providers/Microsoft.Authorization/policyDefinitions/cccc23c7-8427-4f53-ad12-b6a63eb452b3",
"type": "Microsoft.Authorization/policyDefinitions",
"name": "cccc23c7-8427-4f53-ad12-b6a63eb452b3"
}

 

Since this policy is built-in, you can directly assign it without creating your policy definition JSON. To assign this policy using PowerShell, run the following commands:

$policydefinition = Get-AzureRmPolicyDefinition | Where-Object {$_.Properties.DisplayName -like "Allowed virtual machine SKUs"}
New-AzureRmPolicyAssignment -Name testassignment –Scope {scope} -PolicyDefinition $policydefinition -listOfAllowedSKUs "Standard_LRS", "Standard_GRS"

It is this simple now!

Help us improve the experience

 

Please try the new features and provide feedback to us through the user voice. Let us know what policies you want to use and how we can improve the experience.
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Azure Analysis Services adds Standard S0 pricing tier

Over the past several months, we have received positive feedback about the Azure Analysis Services preview. Many customers have moved their models to the cloud and have enjoyed the improved manageability of the platform-as-a-service offering. We have expanded regions where Azure Analysis Services is available, and we are working on several of the improvements asked for on the Azure Analysis Services feedback site.

One scenario customers have asked for is a way to support smaller workloads in the cloud. While you can run multiple databases on a single Standard S1 instance, you may want to start with something smaller based on your model size and query volume. We are introducing a new smaller pricing level, Standard S0, which has 40 QPUs and 10 GB of RAM for models.

This new size offers the same features and capabilities as the rest of the Standard tier. You can continue to scale up and down based on your expected load to achieve the best experience for your users. For example, if you need more RAM when processing data, you can scale up during processing and scale down after processing.  You can also scale up during business hours for better query performance, scale back down in off-hours, or even pause when needed for further cost savings. You can track QPU utilization in the Azure portal and through the Azure Monitoring APIs. Note, your model must fit in the available RAM. It is a good idea to check your memory consumption the Azure Portal to ensure Standard S0 is appropriate for your workload.

Please try out the Standard S0 size and let us know how it works for you. You can share your experiences on the Azure Analysis Services MSDN forum. The forum is also a great place to get help from the community.

New to Azure Analysis Services? Find out how you can try Azure Analysis Services or learn how to create your first data model.
 
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Increasing PolyBase Row width limitation in Azure SQL Data Warehouse

Azure SQL Data Warehouse (SQL DW) is a SQL-based, fully managed, petabyte-scale cloud solution for data warehousing. SQL DW is highly elastic, you can provision in minutes and scale capacity in seconds. You can scale compute and storage independently, allowing you to burst compute for complex analytical workloads or scale down your warehouse for archival scenarios, and pay based off what you're using instead of being locked into predefined cluster configurations.

In the latest release of PolyBase in SQL DW, we have increased the row width limit to 1MB from 32KB. This will allow you to ingest your wide columns directly from Windows Azure Storage Blob or Azure Data Lake Store into SQL DW.

When thinking about loading data into SQL DW via PolyBase, you need to take into consideration a couple key points regarding the data size of strings.

For character types (char, varchar, nchar, nvarchar), the 1MB data size is based on memory consumption of data in UTF-16 format. This means that each character is represented by 2 bytes.
When importing variable length columns ((n)varchar, varbinary), the loading tool pads the buffer to the width of the schema in the external table definition regardless of data type. This means that a varchar(8000) has 8000 bytes reserved regardless of the size of the data in the row.

To help improve performance, define your external table with minimal amount of padding on schema data types to maximize the amount of data transferred per internal buffer.

Additionally, it is a best practice to use a medium or a large resource class and to scale up to a larger DWU instance to take advantage of additional memory needed for importing data, especially into CCI tables. More information can be found at our documentation for Memory allocation by DWU and Resource Class.

Next Steps

Give loading with External Tables into SQL DW a try with our loading tutorial.

Learn More

What is Azure SQL Data Warehouse?

What is Azure Data Lake Store?

SQL Data Warehouse best practices

MSDN forum

Stack Overflow forum
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Announcing Azure SQL Database Premium RS, 4TB storage options, and enhanced portal experience

Today we are happy to announce the preview of the latest edition to our service tiers, Premium RS, a 4TB increase of storage limits for Premium P11 and P15, and along with it a new, enhanced portal experience for selecting and managing service tiers and performance levels.

Adding more choices in our service tiers and increasing the available storage is a crucial step towards reaching our long-term commitment of providing more flexibility. Both for compute as well as storage across all performance tiers, allowing increased flexibility to customers.

Premium RS

Premium RS is designed for your IO-intensive workloads that need Premium performance but do not require the highest availability guarantees. This tier is ideal for workloads can replay the data in case of a severe system error such as analytical workloads where the database is not system of record. In addition, Premium RS is great for non-production databases, such as development using in-memory technologies or pre-production performance testing. For more details refer to the documentation.

4TB storage option in Premium P11 and P15

You can now use up to 4TB of included storage with P11 and P15 Premium databases at no additional charge. Until we have worldwide availability later in CY 2017, the 4TB option can be selected for databases located in the following regions: East US 2, West US, Canada East and South East Asia (all starting March 9th) and West Europe, Japan East, Australia East, Canada Central (available today). For more details refer to the documentation.

Enhanced pricing tier portal experience

We have simplified your pricing tier manageability experience for databases in the portal. The configuration of your database can now be done in three simple steps reflecting the additional options we are providing such as Premium RS and additional storage configurations:

Select the service tier which corresponds to your workload needs.
Select the performance limits (DTU) required by your database.
Select the maximum storage required to your database. This added option hopes to make it simpler for you to manage the growth of your databases.

Next steps:

Review the pricing page for our new offers.
Create a new Premium RS database or elastic pool.
Create a P11 or P15 Premium database with 4TB of storage.

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Azure Service Bus Premium Messaging now available in UK

We’re pleased to announce Azure Service Bus Premium Messaging now available in the UK.

Service Bus Premium Messaging supports a broader array of mission-critical cloud apps, and provides all the messaging features of Service Bus queues and topics with predictable, repeatable performance and improved availability – now generally available in UK.

For more general information about Service Bus Premium Messaging, see this July 2016 blog post and this January 2017 article "Service Bus Premium and Standard messaging tiers".

We are excited about this addition, and invite customers using this Azure region to try Azure Service Bus Premium Messaging today!
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Azure Data Factory February new features update

Azure Data Factory allows you to bring data from a rich variety of locations in diverse formats into Azure for advanced analytics and predictive modeling on top of massive amounts of data. We have been listening to your feedback and strive to continuously introduce new features and fixes to support more data ingest and transformation scenarios. Moving to the new year, we would like to start a monthly feature summary blog series so our users can easily keep track of new feature details and use them right away.

Here is a complete list of the Azure Data Factory updates for February. We will go through them one by one in this blog post.

New Oracle driver bundled with Data Management Gateway with performance enhancements
Service Principal authentication support for Azure Data Lake Store
Automatic table schema creation when loading into SQL Data Warehouse
Zip compression/decompression support
Support extracting data from arrays in JSON files
Ability to explicitly specify cloud copy execution location
Support updating the new Azure Resource Manager Machine Learning web service

New Oracle driver bundled with Data Management Gateway with performance enhancements

Introduction: Previously, to connect to Oracle data source through Data Management Gateway users were required to install the Oracle provider separately, causing them to run into different issues. Now, with the Data Management Gateway version 2.7 update, a new Microsoft driver for Oracle is installed so no separate Oracle driver installation is required. The new bundled driver providers better load throughput, with some customers observing 5x-8x performance increase. Refer to Oracle connector documentation page for details.

Configuration: The Data Management Gateway periodically checks for updates. You can check its version from the Help page as shown below. If you are running a version lower than v2.7, you can get update directly from the Download Center. With Data Management Gateway version 2.7, the new driver will be used automatically in Copy Wizard when Oracle is being used as source. Learn more about Oracle linked service properties.

Service Principal authentication support for Azure Data Lake Store

Introduction: In addition to the existing user credential authentication, Azure Data Factory now supports Service Principal to access the Azure Data Lake Store. The token used in the previous user credential authentication mode could expire after 12 hours to 90 days, so periodically reauthorizing the token manually or programmatically is required for scheduled pipelines. Learn more about the token expiration of data moving from Azure Data Lake Store using Azure Data Factory. Now with the Service Principal authentication, the key expiration threshold is much longer so you are suggested to use this mechanism going forward, especially for scheduled pipelines. Learn more about the Azure Data Lake Store and Service Principal.

Configuration: In the Copy Wizard, you will see a new Authentication type option with Service Principal as default, shown below. 

Automatic table schema creation when loading into SQL Data Warehouse

Introduction: When copying data from On-Premise SQL Server or Azure SQL Database to Azure SQL Data Warehouse using the Copy Wizard, if the table does not exist in the destination SQL Data Warehouse, Azure Data Factory can now automatically create the destination table using schema from source.

Configuration: From the Copy Wizard, in the Table mapping page, you now have the option to map to existing sink tables or create new ones using source tables’ schema. Proper data type conversion may happen if needed to fix the incompatibility between source and destination stores. Users will be warned in the Schema mapping page, as shown in the second image below, about potential incompatibility issues. Learn more about Auto table creation.

 

Zip compression/decompression support

Introduction: The Azure Data Factory Copy Activity can now unzip/zip your files with ZipDeflate compression type in addition to the existing GZip, BZip2, and Deflate compression support. This applies to all file-based stores, including Azure Blob, Azure Data Lake Store, Amazon S3, FTP/s, File System, and HDFS.

Configuration: You can find the option in Copy Wizard pages as shown below. Learn more from the specifying compression section in each corresponding connector topic.

Extracting data from arrays in JSON files

Introduction: Now the Copy Activity supports parsing arrays in JSON files. This is to address the feedback that the entire array can only be converted to a string or skipped. You can now extract data from array or cross apply objects in array with data under root object.

Configuration: The Copy Wizard provides you with the option to choose how JSON array can be parsed as shown below. In this example, the elements in “orderlines” array are parsed as “prod” and “price” columns. For more details on configuration and examples, check the specifying JSON format section in each file-based data store topic.

Ability to explicitly specify cloud copy execution location

Introduction: When copying data between cloud data stores, Azure Data Factory, by default, detects the region of your sink data store and picks the geographically closest service to perform the copy. If the region is not detectable or the service that powers the Copy Activity doesn’t have a deployment available in that region, you can now explicitly set the Execution Location option to specify the region of service to be used to perform the copy. Learn more about the globally available data movement.

Note: Your data will go through that region over the wire during copy.

Configuration: Copy wizard will prompt for the Execution location option in the Summary page if you fall into the cases mentioned above.

Support updating the new Azure Resource Manager Machine Learning web service

Introduction: You can use the Machine Learning Update Resource Activity to update the Azure Machine Learning scoring service, as a way to operationalize the Machine Learning model retrain for scoring accuracy. Now in addition to supporting the classic web service, Azure Data Factory can support the new Azure Resource Manager based Azure Machine Learning scoring web service using Service Principal.

Configuration: The Azure Machine Learning Linked Service JSON now supports Service Principal so you can access the new web service endpoint. Learn more from scoring web service is Azure Resource Manager web service.

 

Above are the new features we introduced in February. Have more feedbacks or questions? Share your thoughts with us on Azure Data Factory forum or feedback site, we’d love to hear more from you.
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Azure Stream Analytics Tools for Visual Studio

Have you had chance to try out the public preview version of the Azure Stream Analytics Tools for Visual Studio yet? If not, read through this blog post and get a sense of the Stream Analytics development experience with Visual Studio. These tools are designed to provide an integrated experience of Azure Stream Analytics development workflow in Visual Studio. This will help you to quickly author query logic and easily test, debug, and diagnose your Stream Analytics jobs.

Using these tools, you get not only the best in class query authoring experience, but also the power of IntelliSense (for code-completion), Syntax Highlighting, and Error Marker capabilities. You now can test queries on local development machines with representative sample data to speed up development iterations. Seamless integration with the Azure Stream Analytics service helps you submitting jobs, monitoring live job metrics easily, and exporting existing jobs to projects with just few clicks. Never the less, you can naturally leverage Visual Studio source control integration capabilities to manage your job configurations and queries.

Use Visual Studio Project to manage a Stream Analytics job

To create a brand-new job, just create a new project from the built-in Stream Analytics template. The job input and output are stored in JSON files, and job query is saved in the Script.asaql file. Double click on input and output JSON files inside the project, you will find out that their setting UI is very similar to the Portal UI.

Using Visual Studio tools to author Stream Analytics queries

Double click on the Script.asaql file to open the query editor, its IntelliSense (code-completion), Syntax Highlighting, and Error Marker make your authoring experience very efficient. Also, if the local input file is specified and defined correctly or you have ever sampled data from the live input sources, the Query Editor will prompt the column names in the input source when the developer enters the input name.

Testing locally with sample data

Upon finishing the query authoring, you can quickly test it on your local machine by specifying local sample data as input. This speeds up your development iterations.

View live job metrics

After you validate the query test result, click on “Submit to Azure” to create a streaming job under your Azure subscription. Once the job is created you can start and monitor your job inside the job view.

View and update jobs in Server Explorer

You can browse all your Stream Analytics jobs under your subscriptions in Server Explorer, expand a job node to view its job status and metrics. If needed, you can directly update job queries and job configurations, or export an existing job to a project.

How do I get started?

You need to first install Visual Studio (2015, Visual Studio 2013 update 4, or Visual Studio 2012) and install the Microsoft Azure SDK for .NET version 2.7.1 or above using the Web platform installer. Then get the latest ASA tools from Download Center.

For more information, can be found at Tutorial: Use Azure Stream Analytics Tools for Visual Studio.
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