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Category: Cloud

Getting Started With Azure ML

Koos van Strien gives a quick overview of Azure ML:

Before I started, I was already quite comfortable programming Python and did some R programming in the past. This turned out pretty handy, though not really needed to start off with – because starting with Azure ML, the data flow can be created much like BI specialists are used to in SSIS.

A good place to start for me was the Tutorial competition (Iris Petal Competition). It provides you with a pre-filled workspace with everything in place to train and test your first ML model:

I’d like to see Azure ML get more traction; I’m not optimistic that it will.

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Migrating To Azure SQL Data Warehouse

Rangarajan Srirangam has a detailed article on steps you should take when migrating a database to Azure SQL Data Warehouse:

This article focuses on migrating data to Azure SQL Data Warehouse with tips and techniques to help you achieve an efficient migration. Once you understand the steps involved in migration, you can practice them by following a running example of migrating a sample database to Azure SQL Data Warehouse.

Migrating your data to Azure SQL Data Warehouse involves a series of steps. These steps are executed in three logical stages: Preparation, Metadata migration and Data migration.

It’s a lengthy read, but well worth it.

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Cloud Security

Kenneth Fisher provides musings based on an Azure security document:

It gives you a map on how to manage your security as you move into the cloud. Note: one of the main points is that your on premise security is equally as important and has to be managed with and as a part of your cloud security.

Now if you are like me and want more than just dry reading they also provide a link to a Microsoft Virtual Academy training course called Security in a Cloud-Enabled World that follows this roadmap and provides more detail and guidance.

Read the whole thing.

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Azure SQL Database Maintenance

Jeffrey Verheul mentions that Azure SQL Database databases need regular maintenance, too:

Before I’m going into detail, I want to give full kudos to Ola Hallengren (Website | @olahallengren). He has spend a lot of his time to build a SQL Server Maintenance Solution that is completely free for everyone to use. And he did such an excellent job a lot companies (also huge companies) use his solution to run maintenance tasks on their databases.

None of the scripts below are written by me, but only small changes are made in order to make things more clear when the solution is deployed to an environment. The original scripts can be downloaded via the download page on Ola’s website.

Most of the to-dos are the same between on-premises and Azure SQL DB, but some of the implementation steps are a bit different.  This is worth checking out if you have any Azure SQL Database instances.

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Getting Started With Azure SQL Data Warehouse

Warner Chaves looks at Azure SQL Data Warehouse:

The first thing to keep in mind is that ASDW was designed to be a cloud based system. As such, it aims to be very flexible for resource allocation and very efficient to scale up or down. To meet those goals, the system allows you too:

  • Increase or decrease compute power represented by Data Warehousing Units.

  • The amount of storage can grow and is charged independently from the compute power.

  • The compute power can be completely paused and only storage is payed at that point.

Warner also has a brand new Pluralsight course on the topic.

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Calling Azure ML Web Services Using Data Factory

Ginger Grant shows how to call an Azure Machine Learning web service from within Azure Data Factory:

The Linked Service for ML is going to need some information from the Web Service, the URL and the API key. Chances are neither of these have been committed to memory, instead open up Azure ML, go to Web Service and copy them. For the URL, look under the API Help Pagegrid, there are two options, Request/Response and Batch Execution. Clicking on Batch Execution loads a new page Batch Execution API Document. The URL can be found under Request URI. When copying the URL, you do not need to include any text after the word “jobs”. The rest of the URL, “?api-version=2.0”. Copying the entire URL will cause an error. Going back to the web Services page, The API Key appears on the dashboard section of Azure ML and there is a convenient button for copying it. Using these two pieces of information, it is now possible to create the Data Factory Linked Service to make the connection to the web service, which here I called AzureMLLinkedService

Read the whole thing.

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Azure SQL Database Alerts

Julie Koesmarno shows how to set up Azure SQL Database alerts:

Over the last year, I have been intentionally seeking out to get feedback from the community via various SQL events, particularly those who plan to use or are currently using Azure SQL Database. A lot of questions have come up about managing Azure SQL Database better – i.e. being more proactive and more responsive in managing Azure SQL Database. One of the ways to be more proactive about your SQL Database is by setting up alerts. As an example, you can create an alert in case DTU goes above 95% – say in the last 5 minutes, so that you can either investigate why this might be or upgrade it to a higher SKU.

This article walks through how you can setup an Alert on Azure SQL DB.

I really like the fact that they offer web hooks; that way, I can integrate these alerts with Slack or other messaging systems.

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Azure SQL Database Versus SQL Server

Kenneth Fisher learns about differences between Microsoft’s Azure SQL Database and their on-premises (or IaaS) SQL Server:

T-SQL Differences in Azure SQL Database

I used to think this was the real difference between SQL Server and SQL Database. I was wrong. Really wrong. But it’s a good place to start. Now from what I can tell everything in Azure is a moving target. There are constant changes so it’s important to know where the documentation is. In this particular case here it is: Azure SQL Database Transact-SQL differences.

Check it out.  The differences are smaller than in the past, but I expect that there will always be some differences—particularly on the administration side—due to the nature of Azure SQL Database as a PaaS offering.

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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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