The Basics of Dynamic Data Masking - Step 1 of the Stairway to Dynamic Data Masking
Learn the basics of how Dynamic Data Masking can be used to obfuscate data in SQL Server 2016+.
Learn the basics of how Dynamic Data Masking can be used to obfuscate data in SQL Server 2016+.
New solution allows developers and teams to “buy the base and rent the peak” when provisioning for variable workloads without wastage or performance tradeoffs
Or perhaps another way to phrase that is, Microsoft is up to something with parallelism. I don’t know how long it’s been this way – could even be since the launch of Azure SQL DB Serverless – but I just now noticed it while helping a client with a slow query.
When we need to be heroes in IT on a regular basis, that's a problem for Steve.
This article will show the basic outline for how Python scripts can access and work with data in SQL Server.
I’ve quietly resolved performance issues by re-writing slow queries to avoid DISTINCT. Often, the DISTINCT is there only to serve as a “join-fixer,” and I can explain what that means using an example.
Speed of delivery and protecting data can often feel incompatible, but there are industry-proven database DevOps practices that bring them together in harmony.
Across each of these five key practices, there’s a theme of removing barriers and cognitive load for teams; but crucially, they are also putting safeguards in place to reduce the risks to production environments.
William Shakespeare once wrote in the play Romeo and Juliet: “What's in a name? That which we call a rose, by any other word would smell as sweet.” It is a beautiful sentiment in that context, and even still you might say that there is some truth to this saying in a lot of ways. […]
Learn how you can configure an Azure Data Factory pipeline that is triggered by an email being sent to an address.
Today Steve looks at the case when one software developer finishes their work, but another doesn't. The challenge of reordering work is something that happens more and more as teams struggle to coordinate their efforts.
Every organization I talk to has the same problem dressed up in different clothes....
By DataOnWheels
I am delighted to host this month’s T-SQL Tuesday invitation. If you are new...
By alevyinroc
Ten years (and a couple jobs) ago, I wrote about naming default constraints to...
Comments posted to this topic are about the item The day-to-day pressures of a...
Comments posted to this topic are about the item Using OPENJSON
Comments posted to this topic are about the item Data Modeling with dbt for...
I have some data in a table that looks like this:
BeerID BeerName brewer beerdescription 1 Becks Interbrew Beck's is a German-style pilsner beer 2 Fat Tire New Belgium Toasty malt, gentle sweetness, flash of fresh hop bitterness. 3 Mac n Jacks Mac & Jack's Brewery This beer erupts with a floral, hoppy taste 4 Alaskan Amber Alaskan Brewing Alaskan Brewing Amber Ale is an "alt" style beer 8 Kirin Kirin Brewing Kirin Ichiban is a Lager-type beerIf I run this, what is returned?
select t1.key
from openjson((select t.* FROM Beer AS t for json path)) t1 See possible answers