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Category: Cloud

Create An Azure SQL Database Instance From Powershell

Arun Sirpal walks through the steps of setting up an Azure SQL Database instance and database using Powershell:

What I have done here is hard-code three parameters ( database edition, start IP address and end IP address) which for my situation won’t change but I have given the ability to pass in the environment name, SQL Server name and database name.

So a prompt will be presented to the user – here you should enter the relevant details and click enter.

It’s not that difficult to do, and the scripts themselves are probably faster than fumbling around the UI.

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Observations On Azure SQL Data Warehouse

Jeffrey Verheul is running this month’s T-SQL Tuesday.  Here is his contribution:

A thing that can make migrations to the cloud a bit more difficult, is that Azure SQL databases are basically a contained datastore (you would call it a “contained database” when you run it on-premise). This means that you (by default) can’t connect from one database to the other. This could mean that you need to rewrite your applications or stored procedures, or maybe even redesign your entire database/application/domain model.

This also means that running a stored procedure from the Ola Hallengren’s maintenance solution can only be done on the specific database, and not from the master database like the on-premise version does. These small challenges can be overcome, but it does mean code-duplication in your databases because the maintenance procedures need to be deployed to every single database.

Read on for more observations regarding Azure SQL Data Warehouse.

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Creating BACPAC Files

Kenneth Fisher needs a new BACPAC:

Why are we talking about it?

Well there are two reasons. First because I’m studying how to move databases from SQL Server to Azure SQL Database and back. My first blog on the subject was using the Deploy Database to Microsoft Azure SQL Database option to move a SQL Server database to Azure SQL Database.

Kenneth shows you how to do this through the UI as well as through SqlPackage.exe.

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Service Fabric On Linux

Mark Russinovich announces that Azure Service Fabric will be available on Linux:

Given its beginnings, Service Fabric supports Windows servers and .NET applications, but many enterprises today run heterogeneous workloads, including Windows and Linux servers, .Net and Java applications, and SQL and NoSQL databases. That’s why I am excited to announce today that the preview of Service Fabric for Linux will be publicly available at our Ignite conference on September 26.  With today’s announcement customers can now provision Service Fabric clusters in Azure using Linux as the host operating system and deploy Java applications to Service Fabric clusters. Service Fabric on Linux will initially be available for Ubuntu, with support for RHEL coming soon.

This isn’t a huge announcement for many people, but it’s a positive sign.

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Thinking About Azure SQL Database

Kevin Hill with an introductory-level discussion of Azure SQL Database:

Some basic terminology:

  • Cloud: No such thing.  It is just your stuff on someone else’s machines that they maintain for you.

  • Virtual Machine (VM): A Virtual Server on some physical servers…yours, or someone else’s.

  • Azure: Fancy name for Microsoft’s cloud. As a noun or an adverb it means “blue”.  Or a small butterfly.

  • Azure SQL database: Just a database in Azure on some storage

  • Azure Virtual Machine: A VM on Microsoft’s Azure servers, that you do not have to maintain the underlying physical infrastructure.

This is a nice, very high-level introduction to why Azure SQL Database exists.

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Query Performance Insight

Arun Sirpal discusses Query Performance Insight in Azure SQL Databases:

Here you will be presented with the TOP X queries based on CPU, Duration or Execution count. You will have the ability to change the time period of analysis, return 5, 10 or 20 queries using aggregations SUM, MAX or AVG.

So let’s look at what information is provided based on queries with high AVG duration over the last 6 hours.

Looks like an interesting way to get information on the few most heavily used queries.

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Troubleshooting Event Hub Issues

Ginger Grant walks through a couple of issues you might run into with Event Hub:

The input for this stream is set to an event hub which has a standard subscription. The basic subscription, which is of course cheaper, has one default consumer group. With a standard subscription multiple consumer groups can be created and more importantly named. When setting up the inputs there is a blank for the name of the consumer group. If you have a basic subscription this will be empty. If it is empty, then the event hub won’t pass data to the stream analytics job. Perhaps there is a way to get a basic event hub to work with a stream analytics job, but I couldn’t make it happen. When I created an event hub with a standard subscription and created a consumer group and added that name to the input of a streaming analytics job, it worked.

Read on for details.

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Azure ML To Python

Koos van Strien “graduates” from Azure ML into Python:

Python is often used in conjunction with the scikit-learn collection of libraries. The most important libraries used for ML in Python are grouped inside a distribution called Anaconda. This is the distribution that’s also used inside Azure ML1. Besides Python and scikit-learn, Anaconda contains all kinds of Data Science-oriented packages. It’s a good idea to install Anaconda as a distribution and use Jupyter (formerly IPython) as development environment: Anaconda gives you almost the same environment on your local machine as your code will run in once in Azure ML. Jupyter gives you a nice way to keep code (in Python) and write / document (in Markdown) together.

Anaconda can be downloaded from https://www.continuum.io/downloads.

If you’re going down this path, Anaconda is absolutely a great choice.

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Automatic Approval For Data Lake Analytics

Yan Li reports that Azure Data Lake Analytics no longer requires waiting for approval:

We’re happy to announce that we’ve made it much faster to get started with the Data Lake Store and Analytics services starting today. Before today, when you tried to sign up for these services you had to go through an approval process that introduced a delay of at least one hour.

Now, you no longer have to wait for approval, and you can simply create an account immediately.

Yan also has some “getting started” links to help you out, now that you don’t have to wait for an account.

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Azure SQL Database Supports JSON

Jovan Popvic reports that Azure SQL Database now has full JSON support:

JSON is available in all service tiers (basic, standard, and premium) but only in new SQL Database V12. You can see quick  introduction here or more details in Getting Started page. you can also find code samples that JSON functions in Azure Sql Database on official Sql Server/Azure Sql Database GitHub repository.

Note that OPENJSON function requires database compatibility level 130. If all functions work except OPENJSON, you would need to set the latest compatibility level in database.

It will be interesting to see adoption of JSON within Azure SQL Database.  I could see it being a bit more likely due to DocumentDB.

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