A Fine Slice Of SQL Server
Arun Sirpal discusses ways to restore a database within Azure SQL Database:
You won’t have the ability to use the same name of the restoring database and the database that you want to replace; if you try you get the screen shot below: To get around this I think you would need to drop the old one once the new one has restored then do a rename.
This is a big difference compared to the on-prem version, so be sure to practice this before you find yourself in a crisis.
Comments closedMike Fal discusses BACPACs, DACPACs, and migrating on-prem databases to Azure SQL Database:
SQL Server Data Tools(SSDT) have always had a process to extract your database. There are two types of extracts you can perform:
DACPAC – A binary file that contains the logical database schema and possibly the data. This file retains the platform version of the database (i.e. 2012, 2014, 2016).
BACPAC – A binary file that contains the logical database schema and the data as insert statements. This stores the platform version, but is not locked into it.
Mike also walks through SqlPackage.exe.
Comments closedWhat 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.
Comments closedJeffrey 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.
Comments closedKenneth 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.
Comments closedMark 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.
Comments closedKevin 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.
Comments closedArun 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.
Comments closedGinger 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.
Comments closedKoos 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.
Comments closed