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Category: Cloud

Microsoft R Server On Spark

Max Kaznady, et al, discuss using Microsoft R Server on Spark to perform rapid prototyping against the NYC Taxi dataset:

Once the cluster is created, you can connect to the edge node where MRS is already pre-installed by SSHing to r-server.YOURCLUSTERNAME-ssh.azurehdinsight.net with the credentials which you supplied during the cluster creation process. In order to do this in MobaXterm, you can go to Sessions, then New Sessions and then SSH.

The default installation of HDI Spark on Linux cluster does not come with RStudio Server installed on the edge node. RStudio Server is a popular open source integrated development environment (IDE) available for R that provides a browser-based IDE for use by remote clients. This tool allows you to benefit from all the power of R, Spark and Microsoft HDInsight cluster through your browser. In order to install RStudio you can follow the steps detailed in the guide, which reduces to running a script on the edge node.

If you’ve been meaning to get further into Spark & R, this is a great article to follow along with on your own.

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Azure SQL Threat Detection

Ron Matchoro discusses use cases for Azure SQL Threat Detection:

Thanks to SQL Threat Detection, we were able to detect and fix code vulnerabilities to SQL injection attacks and prevent potential threats to our database. I was extremely impressed how simple it was to enable threat detection policy using the Azure portal, which required no modifications to our SQL client applications. A while after enabling SQL Threat Detection, we received an email notification about ‘An application error that may indicate a vulnerability to SQL injection attacks’.  The notification provided details of the suspicious activity and recommended concrete actions to further investigate and remediate the threat.  The alert helped me to track down the source my error and pointed me to the Microsoft documentation that thoroughly explained how to fix my code.  As the head of IT for an information technology and services company, I now guide my team to turn on SQL Auditing and Threat Detection on all our projects, because it gives us another layer of protection and is like having a free security expert on our team.”

Anything which helps kill SQL injection for good makes me happy.

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Considerations For Azure SQL Database

Grant Fritchey discusses whether new database administrators might want to start with Azure SQL Database rather than on-premises SQL Server:

Since you are right at the start of your career, you may as well plan on maximizing the life of the knowledge and skills you’re building. By this, I mean spend your time learning the newest and most advanced software rather than the old approach. Is there still work for people who only know SQL Server 2000? Sure. However, if you’re looking at the future, I strongly advocate for going with online, cloud-based systems. This is because, more and more, you’re going to be working with online, connected, applications. If the app is in the cloud, so should the data be. Azure and the technologies within it are absolutely the cutting edge today. Spending your limited learning time on this technology is an investment in your future.

This answer is a tougher call for me.  Looking at new database developers (or development DBAs or database engineers or whatever…), I think the case is pretty solid:  there’s so much skill overlap that it’s relatively easy to move from Azure SQL Database to on-prem.  With production DBAs, the story’s a little different:  as Grant mentions, this is a Platform as a Service technology, and so the management interface is going to be different.  There are quite a few commonalities (common DMVs, some common functionality), but Grant gives a good example of something which is quite different between the PaaS offering and the on-prem offering:  database backup and restoration.  I think the amount of skills transfer is lower, and so the question becomes whether the marginal value of learning PaaS before IaaS/on-prem is high enough.  Given my (likely biased) discussions of Azure SQL Database implementations at companies, I’d stick with learning on-prem first because you’re much more likely to find a company with an on-prem SQL Server installation than an Azure SQL Database.

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Power BI Embedded

Reza Rad looks at Power BI Embedded:

Power BI Embedded is the reporting and analysis solution for mobile and web applications. Power BI Embedded is an Azure service that integrates Power BI solution into mobile and web applications. The report still has to be authored and created in Power BI Desktop. After creating the report it can be published into Power BI workspace in Azure, and using API Keys of Power BI workspace and embedding Power BI report frame into the web/mobile application it will be integrated into the application.

Reza walks through the process step-by-step.  The upshot is that you can take this report you created in Power BI and embed it into your own application, where you can apply your own in-app access controls.  There are limitations, which Reza spells out at the end of the post, so check it out.

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Credentials In Azure Automation

Mark Vaillancourt explains Azure automation credentials:

With that String parameters, it is easy to just type what I want in the textbox. But, for the PSCredential parameter at the bottom, I was unsure what to do. I figured, since this is just a textbox, I couldn’t just magically pass in a Credential Asset. So, I tried passing in an expression that used the Get-AzureRmAutomationCredential cmdlet that returns the Credential Asset specified. That didn’t work very well. I then started digging through documentation, figuring I would see references and examples of passing a Credential Asset in the Test Pane. It took me a bit, but I finally landed on an Azure.com blog post titled Azure Automation: Runbook Input, Output, and Nested Runbooks. While this post didn’t match all that closely with the search criteria around the Test Pane, it did contain my answer in a section on starting Runbooks:

The answer turns out to be pretty simple.

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Azure SQL Data Warehouse Date Dimensions

Meagan Longoria shows how to create a date dimension in Azure SQL Data Warehouse:

Most data warehouses and data marts require a date dimension or calendar table. Those of us that have been building data warehouses in SQL Server for a while have collected our favorite scripts to build out a date dimension. For a standard date dimension, I am a fan of Aaron  Bertrand’s script posted on MSSQLTips.com. But the current version (as of Aug 8, 2016) of Azure SQL Data Warehouse doesn’t support computed columns, which are used in Aaron’s script.

Click through for the script.

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Copying Data With Data Factory

Ginger Grant shows how to copy data from an Azure SQL Database to Azure Blob Storage using Data Factory:

Because we need a connection to a database and a Azure Blob, two Linked Services are required, one for each different type. Prior to completing this step, create an Azure Blob storage account by clicking on Add on All Resources. Create the second Linked service, like the first. Click on New data store then select Azure Storage. Using the template for an Azure Blob Storage linked services, I have modified it below adding the “hubName” as it is required

There’s a lot of JSON to write here, if you’re into that sort of thing.

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Optimizing HBase In HDInsight

Ashish Thapliyal links to a 30-minute presentation on HBase optimization:

This session was presented by Nitin Verma (Sr. Software Engineer) and Pravin Mittal (Principal Engineering Manager) @ HBaseCon 2016. The session goes deeper into success story of enabling a big internal customer on HDInsight HBase.

HBase design is a totally different mindset from relational design, so you have to unlearn a lot of habits when moving over to it.

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Azure ML Updates

David Smith walks us through new language engines supported in Azure ML:

ML studio now gives you even more flexibility, with new language engines supported in the language modules. Within the Execute Python Script module, you can now choose to use Python 2.7.11 or Python 3.5, both of which run within the Acaconda 4.0 distribution. And within the Execute R Script module, you can now choose Microsoft R Open 3.2.2 as your R engine, in addition to the existing CRAN R 3.1.0 engine. Microsoft R Open 3.2.2 not only gives you a newer R language engine, it also gives you access to a wealth of new R packages for use within ML Studio. Over 400 packages are pre-installed for use with the R Script module, and you can install and use any other R package (including CRAN packages and your own R packages) via the Script Bundle input port.

I’m interested in the Microsoft R Open language support, as Azure ML’s still using a relatively older version of R (3.1.0).

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vNet Peering Within An Azure Region

Denny Cherry reports that there is a public preview of a feature to allow vNet peering without setting up a site-to-site VPN connection:

Up until August 1st if you had 2 vNets in the same Azure region (USWest for example) you needed to create a site to site VPN between them in order for the VMs within each vNet to be able to see each other.  I’m happy to report that this is no longer the case (it is still the default configuration).  On August 1st, 2016 Microsoft released a new version of the Azure portal which allows you to enable vNet peering between vNets within an account.

Now this feature is in public preview (aka. Beta) so you have to turn it on, which is done through Azure PowerShell. Thankfully it uses the Register-AzureRmProviderFeature cmdlet so you don’t need to have the newest Azure PowerShell installed, just something fairly recent (I have 1.0.7 installed). To enable the feature just request to be included in the beta like so (don’t forget to login with add-AzureRmAccount and then select-AzureRmSubscription).

Read the whole thing for details on how to enroll in this feature and how to set it up.

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