A Fine Slice Of SQL Server
Erik Darling starts a new series:
This aligns my stored procedures with the necessary settings to accomplish a couple things:
- Allow the optimizer to use indexed views, filtered indexes, and computed columns
- Avoid errors when modifying tables involved with indexed views, filtered indexes, and computed columns
Click through to see what Erik sets by default. It’s a good list, though a bit more than I think to do. Probably because I haven’t been burned enough yet.
Comments closedStephanie Bruno doesn’t need a built-in radar chart visual:
Radar (or spider) charts are a way to look at multiple metrics, perhaps with a different range of values for each metric, on a single chart. In this example, we’ll look at characteristics of Taylor Swift songs from a Spotify dataset (I have a daughter who still hasn’t forgiven me for not getting tickets to the Eras tour, so hopefully this will make up for it). A matrix with the radar SVG allows us to quickly compare these song characteristics (you can get the dataset and the descriptions of the characteristics here). There are existing radar/spider custom visual charts that are great, but none of them currently have a small multiple option, so we can’t use them to create the visual below, for example.
Click through to see the full example.
Comments closedPhil Seamark builds a notebook:
A few blogs back I shared a technique using Power BI Profiler (or VS Code) to run and capture a trace over a refresh of a Power BI semantic model (the object formally known as a dataset).
I’ve since received a lot of positive feedback from people saying how useful it was to visualize each internal step within a problematic Power BI refresh. Naturally, in the age of Fabric, I’m keen to share how the same approach works using a Microsoft Fabric Notebook.
Click through to see how you can do it.
Comments closedMatt Eland tries out Semantic Kernel:
Generative AI systems use large language models (LLMs) like OpenAI’s GPT 3.5 Turbo (ChatGPT) or GPT-4 to respond to text prompts from the user. But these systems have serious limitations in that they only include information baked into the model at the time of training. Technologies like retrieval augmentation generation (RAG) help overcome this by pulling in additional information.
AI orchestration frameworks make this possible by tying together LLMs and additional sources of information via RAG. Additionally, AI orchestration systems can provide capabilities to generative AI systems, such as inserting records in a database, sending emails, or calling out to external systems.
In this article we’ll look at the high-level capabilities building AI orchestration systems in C# with Semantic Kernel, a rapidly maturing open-source AI orchestration framework.
Click through to see how things work.
Comments closedKevin Wilkie does some soul searching, or at least string searching:
To enjoy these puzzles, you will need to go to the Official Advent of Code website, sign up for their leaderboards and whatnot if you choose to, and then continue to 2023 and Day 1. Today, we’ll start with Day 1 – since it is the first of our programming puzzles and work our way up from there…
We’re asked – given a string – to find the first (and last) number in that string. We are then to concatenate them, add them all up, and provide the result. It should be pretty simple, but let’s see…
Click through for Kevin’s two answers.
Comments closedTomaz Kastrun continues a series on Microsoft Fabric:
If you have used Power BI services in the past, you will be on board immediately. The outlook is the as it is with the Power BI. You will only need additional credentials to access the services. In general, you will need Azure subscription, Power BI service already enabled, and the ability for your organization to enable Fabric with Admin roles
Click through to see how to enable Microsoft Fabric in your environment.
Comments closedLance Wright shows off a dashboard:
At Ignite 2023, we announced the public preview of performance dashboards for SQL Server enabled by Azure Arc. With these performance dashbaord, DBAs and IT Admins now get performance monitoring right from within Azure. No need to setup and login to another tool or remotely connect to the SQL Server to run performance queries. Let Azure Arc do the heavy lifting so you can get to your performance metrics faster.
This is another tool in the toolbox of DBAs, IT admins, and cloud specialists looking to gain better visibility into their hybrid and multi-cloud workloads. If a SQL Server enabled by Azure Arc meets the requirements for data collection (see “How to enable performance dashboards”), Azure Arc will automatically collect the following types of data from the Dynamic Management Views (DMV) datasets oof the SQL Server:
Click through to see what it includes and how to enable it.
Comments closedSean Gallardy talks about an error:
Contained Availability Groups are the most recent update to the Availability Groups feature, and a great update at that! They are completely new in SQL Server 2022, and like any new feature (or even mature ones) there will be some bugs. Enter in some Access Violations (AVs) that may occur when creating a new contained availability group.
Read on for an example of the error and what you can do until Microsoft fixes it.
Comments closedWolfgang Strasser talks legal turkey:
What I even like more is that Microsoft Fabric is now part of the EU Data Boundary Services – i.e. “For EU Data Boundary Services, Microsoft will store and process Customer Data (including any Personal Data contained therein) within the EU Data Boundary as detailed below.”
Read on to see what this means for European customers.
Comments closedPeter Laurinec builds a model to test a model:
In the last blog post about Multistep forecasting losses, I showed the usage of the fantastic method
adamfrom thesmoothR package on household electricity consumption data, and compared it with benchmarks.Since I computed predictions from 10 methods/models for a long period of time, it would be nice to create some ensemble models for precise prediction for our household consumption data. For that purpose, it would be great to predict for example future errors of these methods. It is used in some known ensemble methods, which are not direct about stacking. Predicting errors can be beneficial for prediction weighting or for predicting the rank of methods (i.e. best one prediction). For the sake of learning something new, I will try multivariate regression models, so learning from multiple targets at once. At least, it has the benefit of simplicity, that we need only one model for all base prediction models.
Click through for Peter’s process. H/T R-Bloggers.
Comments closed