It seems straightforward, but as of today, not all builds will enable you to rush out and convert to TLS 1.2 exclusively. Here is what I suggest for each set of builds (in addition to patching .NET Framework, SQL Server Native Client, ODBC, and JDBC on all machines)
A protocol change seems like a small thing, but it suddenly gets to be a big thing when services stop working.
In the architecture above, replica A and B are in the primary data center while replica C and D are in the disaster recovery (DR) site. Like the previous architecture, the disks are displayed as local but the most important part is that they are physically separate. SANs are wonderful systems with a lot of redundancy but they can also be a single point of failure. Keep your Availability Group disks separate.
This is a nice architectural overview. Once the series is done, it looks like it’ll be a good resource to discuss high availability and disaster recovery with management and show the options and trade-offs.
Here is the start of the function. I validate the VMName parameter to ensure that there a VM with that name does not already exist
Before Rob’s posts, I hadn’t seen Pester before, but if you write a lot of Powershell code, it looks like a nice testing framework.
As a conclusion: You CAN use “OVER” clause to do the aggregation in three following cases:
1. When data set is extremely small and fits in just one 8 Kb page;
2. When you want to hide your logic from any future developer or even yourself to make debugging and troubleshooting a nightmare;
3. When you really want to kill your SQL Server and its underlying disk system;
That conclusion’s rather pessimistic for my tastes, mostly because Slava’s trying to do the same thing with a window function that he’s doing with a GROUP BY clause and has multiple functions across different windows (including calculations). Using SUM() OVER() is powerful when you still need the disaggregated values—for example, running totals.
Power BI can connect to many data sources as you know, and Spark on Azure HDInsight is one of them. In area of working with Big Data applications you would probably hear names such as Hadoop, HDInsight, Spark, Storm, Data Lake and many other names. Spark and Hadoop are both frameworks to work with big data, they have some differences though. In this post I’ll show you how you can use Power BI (either Power BI Desktop or Power BI website) to connect to a sample of Spark that we built on an Azure HDInsight service. by completing this section you will be able to create simple spark on Azure HDInsight, and run few Python scripts from Jupyter on it to load a sample table into Spark, and finally use Power BI to connect to Spark server, load, and visualize the data.
If you’re totally unfamiliar with Spark but interested in data processing, now’s a good time to start digging into the topic.
sp_procoption is a system stored procedure that lets us change the options on a stored procedure and in this case set it to run on startup. Note: The account that is running SQL Server needs to have permissions to start the Agent service. (Or do whatever your startup stored procedure does.) You can have as many stored procedures running on startup as you want but remember the more you have the longer it’s going to take for your instance to start.
There are a few uses cases in which startup stored procedures can be useful, but my reservation about them is similar to my reservation about triggers: it’s not apparent to people that a startup stored procedure is in place, so if there is a problem with it, troubleshooting might be harder than normal without good documentation.