Currently, when utilizing the SQL Server images in the VM Gallery in Azure, any installations of SQL Server Analysis Services default to Multidimensional. Thus, if you want SSAS Tabular, you have additional work to perform.
I was just chatting with a Senior Program Manager on the SQL Server Analysis Services product team. They currently don’t have anything in their plans for providing SQL Server Gallery Images with SSAS Tabular instead of Multidimensional. We agreed that it is a good idea for that to happen. We also agreed that a Connect suggestion would be a great way to gauge broader community support/appetite for providing Gallery images with Tabular installed.
When you create VM from a captured image, the drive letters for data disks may not preserved. For example if you have system database files on E: drive, it may get swapped to H: drive. If this is the case, SQL Server can’t find system database files and will not start. If the driver letter mismatch occurs on user database files, then the user databases will not recover. After VM is created, you just need to go to disk management to change the drive letters to match your original configuration.
Read the whole thing if you’re thinking about copying your on-premise server to an Azure VM.
Prior to learning about this new billing method for DSU, I could make the argument that using Stretch Database would be a very cost effective method for storing cold data (unused data) into the cloud. By stretching this data into Azure, you could migrate a large portion of older data, which would decrease the size (and thus cost) of your local backups. In the event you had to restore a database, you would simply have to establish the connection to Azure for the stretched data, thus eliminating the need to restore it. However, with the minimal cost being nearly $1,000 per month for the low end DSU scale, many organizations will find that it is much cheaper to retain the data on a less expensive tier of storage within their data center and find other methods for HA such as mirroring, log shipping, or Availability Groups.
Read the whole thing. Maybe V2 of stretch databases will fix some of the biggest problems (the cost, needing to pull all of your data back down before you make any schema changes, etc.) and become a viable feature, but I can’t see it being one today.
The HDInsight Tool for Eclipse extends Eclipse to allow you to create and develop HDInsight Spark applications and easily submit Spark jobs to Microsoft Azure HDInsight Spark clusters using the Eclipse development environment. It integrates seamlessly with Azure, enabling you to easily navigate HDInsight Spark clusters and to view associated Azure storage accounts. To further boost productivity, the HDInsight tool for Eclipse also offers the capability to view Spark job history and display detailed job logs.
Check out the link for videos and additional resources.
In this post, we focus on sourcing R and Python’s external dependencies, such as R libraries and Python modules, which are not already installed on Azure ML and require code compilation. Commonly the compiled code comes from a variety of other languages such as C, C++ and Fortran. One could also use this approach to wrap their compiled code with R or Python wrappers and run it on Azure ML.
To illustrate the process, we will build two MurmurHash modules from C++ for R and Python using the following two implementations on GitHub, and link them to Azure ML from a zipped folder
Link via David Smith. I knew it was possible to call compiled C code from Python and R, but didn’t expect to be able to do it within Azure ML, so that’s good to know.
Register only data sources that users interact with. Usually the first priority is to register data sources that the users see-for instance, the reporting database or DW that you want users to go to rather than the original source data. Depending on how you want to use the data catalog, you might also want to register the original source. In that case you probably want to hide it from business users so it’s not confusing. Which leads me to the next tip…
Use security capabilities to hide unnecessary sources. The Standard (paid) version will allow you to have some sources registered but only discoverable by certain users & hidden from other users (i.e., asset level authorization). This is great for sensitive data like HR. It’s also useful for situations when, say, IT wants to document certain data sources that business users don’t access directly.
This is a good set of advice.
If you look at the problem, you will at first notice that you want to define something like a user-defined aggregation to combine the overlapping time intervals. However, if you look at the input data, you will notice that since the data is not ordered, you will either have to maintain the state for all possible intervals and then merge disjoint intervals as bridging intervals appear, or you need to preorder the intervals for each user name to make the merging of the intervals easier.
The ordered aggregation is simpler to scale out, but U-SQL does not provide ordered user-defined aggregators (UDAGGs) yet. In addition, UDAGGs normally produce one row per group, while in this case, I may have multiple rows per group if the ranges are disjoint.
Luckily, U-SQL provides a scalable user-defined operator called a reducer which gives us the ability to aggregate a set of rows based on a grouping key set using custom code.
There are some good insights here, so read the whole thing.
What is it
Fully Managed Service (PaaS) for ingesting events/messages at a massive scale (think telemetry processing from websites, IoT etc).
What does it do in our wind farm
Provides a “front door” to our wind farm application to accept all of the streaming telemetry being generated from the turbines. Event Hubs wont process any of this data per se – its just ensuring that its being accepted and queued (short term) while other components cane come in to consume it.
Before you dig deeply into particular services, it’s nice to see how they fit together at a higher level.
I’ve noticed on several occasions that my first attempt to connect to an Azure Sql Server using SQL Server Management Studio 2016 doesn’t always succeed.
The fix? Press OK and try again.
I’ve not noticed this issue myself, so it does seem weird.
Any Select query fails with the following error.
Msg 106000, Level 16, State 1, Line 1
Java heap space
Illegal input may cause the java out of memory error. In this particular case the file was not in UTF8 format. DMS tries to read the whole file as one row since it cannot decode the row delimiter and runs into Java heap space error.
Convert the file to UTF8 format since PolyBase currently requires UTF8 format for text delimited files.
I imagine that this page will get quite a few hits over the years, as there currently exists limited information on how to solve these issues if you run into them, and some of the error messages (especially the one quoted above) have nothing to do with root causes.