A Fine Slice Of SQL Server
Chad Callihan does not fall astray of Betteridge’s Law of Headlines:
Query Store is useful for forcing a beneficial plan. What if something changes that makes the forced plan impossible to use? Let’s look at an example of that today.
Read on for an example. The answer is, I believe, the best possible outcome given the circumstances—certainly better than a hard failure.
Comments closedMatt Allington looks at some recent updates to Power BI:
I’ve been pretty busy over the last few months. The demand for Power BI skills has never been stronger, and my company is super busy. I haven’t written a blog article for a while, but I wanted to take a bit of time out this morning to talk about the August 2022 update to Power BI Desktop. As Power BI matures, there is less and less to get excited about with a new release of Desktop, but there were a couple of things that caught my eye in this release, worthy of calling out.
Read on for a couple of quality of life improvements.
Comments closedGilbert Quevauvilliers is a cultured individual:
I was recently working with an overseas customer and when I tried to open the PBIX file I got the following error
“Culture Name ‘en-IL” is not valid or is not supported”
Read on to understand what the problem was as well as how Gilbert was able to fix it.
Comments closedJames Serra puts together a compendium:
Recently there has been a number of great articles published on Power BI that I wanted to make you aware of that go beyond the features descriptions found in the Power BI documentation. These new articles fall under the Power BI guidance documentation and are designed to address common strategic patterns. Below is my summary of the articles, and check out Power BI guidance from the CAT by Matthew Roach for a more detailed summary.
If you’re interested in Power BI administration and strategic deployment, there’s a lot of good information here.
Comments closedChen Hirsh counts down the days:
Recently Microsoft announced a new DAX function to calculate working days, meaning days between 2 dates, ignoring weekends and (optionally) holidays.
Click through for more information on this function and how to define your working days.
Comments closedMichael J. Swart does a bit of shuffling:
Typically as developers, we don’t care about these values. But when we do want to dig into the transaction log, we can do so with
sys.fn_dblogwhich takes two optional parameters. These parameters are LSN values which limit the results ofsys.fn_dblog. But the weird thing is thatsys.fn_dblogis a function whose LSN parameters are NVARCHAR(25).The function
sys.fn_dblogdoesn’t expect binary(10) values for its LSN parameters, it wants the LSN values as a formatted string, something like:0x00000029:00001a3c:0002.
Never fear, though: Michael’s got us covered. Click through for a conversion function.
Comments closedFranco Patano generates some identity integers:
Identity columns solve the issues mentioned above and provide a simple, performant solution for generating surrogate keys. Delta Lake is the first data lake protocol to enable identity columns for surrogate key generation.
Delta Lake now supports creating
IDENTITYcolumns that can automatically generate unique, auto-incrementing ID numbers when new rows are loaded. While these ID numbers may not be consecutive, Delta makes the best effort to keep the gap as small as possible. You can use this feature to create surrogate keys for your data warehousing workloads easily.
This is a bit light on explanation, unfortunately. With distributed systems, generating identities is historically tricky (especially with several independent nodes generating values) so I’d be curious to see how it works: do they allocate blocks of IDs to worker nodes or do something else? And are the IDs guaranteed to be monotonically increasing? Or is there some other service which “labels” the data upon insert and provides those IDs?
Comments closedDurgesh Gupta provides a primer on the humble decision tree:
A decision tree is a graphical representation of all possible solutions to a decision.
The objective of using a Decision Tree is to create a training model that can use to predict the class or value of the target variable by learning simple decision rules inferred from training data.
It is a tree-structured classifier, where internal nodes represent the features of a dataset, branches represent the decision rules and each leaf node represents the outcome.
The way I like to describe decision trees, especially to developers, is that a tree is a set of if-else statements which leads to a conclusion. The nice part about decision trees is that once you understand how they work, you’re halfway there to gradient boosting (e.g., XGBoost) and random forests.
Comments closedRobert Cain continues a series on KQL:
Performing DateTime arithmetic in Kusto is very easy. You simply take one DateTime data type object and apply standard math to it, such as addition, subtraction, and more. In this post we’ll see some examples of the most common DateTime arithmetic done when authoring KQL.
Read on for several examples of how it all works.
Comments closedSarah Dutkiewicz continues a series on learning Azure. First up, Azure Functions:
Azure Functions are not something you’ll see rendered on a front-end somewhere. They’re a serverless solution used for doing things in the back-end and the middle tier.
After that, Sarah touches on database options:
There are many databases on Azure – including relational data in Azure SQL, NoSQL with Azure Cosmos DB, and even some popular databases in the open source realm such as MySQL and PostgreSQL. These are just a few of the data stores available. Check this page of Azure Databases for a matrix of the databases available compared by their features.
Click through for quite a few links and information on when to use what.
Comments closed