I’ve been playing around with SQL Server running in Kubernetes in Azure Container Services (AKS)for a while now and I think that the technology is really cool.
You can get a highly available instance of SQL Server up and running with a few lines of code! Ok, there’s a bit of setup to do but once you get that out of the way, you’re good to go.
One thing that has been missing though, is persistent storage. Any changes made to the SQL instance would have been lost if the pod that it was running in failed and was brought back up.
Click through to learn how. It’s certainly not trivial, but Andrew does a good job showing us the step-by-step.
In this module you will learn how to use the TreeViz Custom Visual. The TreeViz is a breakdown tree that allows you to expand or collapse levels of hierarchical data.
Click through for the video, showing more. For limited, hierarchical, categorical data, this could work pretty well.
According to MSDN – The DATE_CORRELATION_OPTIMIZATION database SET option improves the performance of queries that perform an equi-join between two tables whose date or datetime columns are correlated, and which specify a date restriction in the query predicate.
How many of you read what MSDN says and thinks “wuuuuuttt, English please”? I do.
Read on for the English translation.
A Self Service culture will allow to address analysts to generate their own reports, lists, and dashboards without dependence on the schedule and availability of IT staff. In these cases reports combine different sources of information are generated, many of which may not have been used historically in the company, and this in turn implies that a large number of cases which source you do not know used to implement certain reports.
Azure Data Catalog comes as an option to break that cycle of discovery that is usually done manually. This means that after the first cycle where the business analyst discovers the sources of optimal data to generate certain reports the can register, and add information (metadata) to make this source easier to discover future analysts requiring such data for the implementation of similar reports. The discovery of these sources, and capability to add metadata are procedures do not have to give at the same time but Data Catalog allows work annotations by analysts as a continuous work in time where more information is added to the repository every time.
Click through for a demo.
Then as far as SQL Server is concerned you took a backup, but the backup file is never actually created.
So why would you ever want to do that? SQL thinks you took a backup, but you have nothing to recover from. Sounds a bit, well, stupid, doesn’t it? Well, there are a few reasons.
Bonus points for Andrew Notarian’s comment.
Still … there is a trick here, and I don’t see a lot of people using it. Maybe it’s just me, maybe I’m lazier than the average dev, but I often find myself using the Script Table As menu and choosing SELECT To and Clipboard. This creates a nice select statement with all my fields wrapped in hard brackets. I can then copy this into an INSERT query I might be working on to save myself some typing. I can quickly copy the field list from the Query ‘Script Table As’ gives me and use it in the top of my INSERT query, then I can copy the entire SELECT query into the bottom of my INSERT query and Bob’s yer Uncle, I’ve got a simple INSERT query ready to go. Note: This is most useful when I’m trying to create a new table based on an existing table with only minor changes to field names. I use this frequently when I’m establishing a reporting database based on staging tables.
That’s three ways to do it in Management Studio; the next step in the process is using SMO to script using a .NET language (C#, F#, Powershell).
Well at PASS Summit, both in our booth and during my presentation on security in Azure DB, another idea came up—exporting data from production to development, while not releasing any sensitive data. This is a very common scenario—many DBAs have to export sensitive data from prod to dev, and frequently it is done in an insecure fashion.
Doing this requires a little bit of trickery, as dynamic data masking does not work for administrative users. So you will need a second user.
Read on for details.
Perhaps speculation feels like the right topic today because Microsoft folks talked a lot about the importance of prediction in the keynotes at the PASS Summit last week.
SQL Server 2016 features R Services. This brings the ability to learn patterns and make predictions into the database engine.
Using this new feature came up a lot in the keynote. And not just for performing predictions for a user application, either: there were quite a few references about using SQL Server’s predictive powers to make SQL Server itselfsmarter.
So what might that mean?
It may be speculation, but it’s quite interesting.
It doesn’t have to be so bad, though, because we can make it better. In my mind, my perfect error log cmdlet should:
- Be usable when a SQL Server instance is down, and
- Be relatively quick, and
- Parse error messages by number, severity, and state, and
- Incorporate date/time range filtering, and
- Return an object
What’s that, you say? You want to solve each of these and write our own, better cmdlet? I got you, fam. At the bottom of this blog post you’ll find my code, and you can skip ahead if you don’t care about the “hows” or “whys” of what you’ll be looking at.
Very nice work Drew did in putting this together.
The basic idea here is “alert me if something has changed dramatically.” If there’s a corner of my business that has spiked or crashed in a big way, I want to know. If something has dramatically improved in a particular region, I may want to dive into that and see if it’s something we can replicate elsewhere. And if something has fallen off a cliff, well, I need to know that for obvious reasons too. And both kinds of dramatic change, positive and negative, can easily be obscured by overall aggregate values (so in some sense this is a similar theme to “Sara Problem?”)
So the first inclination is to evaluate distance from average performance. And maybe that would be fine with high-volume situations, but when we’re subdividing our business into hundreds or perhaps thousands of micro-segments, we end up looking at smaller and smaller sample sizes, and “normal” variation can be legitimately more random than we expect.
This looks really cool. If you read the comments, Rob notes that performance does break down at some point. If you start hitting that point, I’d think about shifting this to R.