A Fine Slice Of SQL Server
Rob Sewell provides a primer on the Fabric Cost Analysis tool:
Today we look at another monitoring tool in the collection: FCA, the Fabric Cost Analysis solution.
Where FUAM answers “how is my Fabric capacity being used?”, FCA answers “what is it costing us, and who is spending what?”
Click through to see how it works.
Comments closedMiles Cole shares some advice:
To show the impact I want to highlight a benchmark that included Fabric Spark on a single 19GB CSV input file (100M Contoso dataset, sales table) for the benchmark. While there were a number of issue with this benchmark that inadvertently make Spark appear to be slow, this is only focused on the impact of inferring schema and practical recommendations.
Read on to see a performance problem that schema inference brings up. I’d also want to mention the risk of data updates blowing up your well-laid plans as a risk. Schema inference is a double-edged sword: it can be convenient and open up new approaches to development, but can just as easily cause unexpected failures.
Comments closedRob Sewell lays out some information:
When you are responsible for a Microsoft Fabric tenant, it will not be very long before you are facing many questions.
Questions like:
- How is my capacity being used?
- Which workspaces are consuming the most resources?
- What are my users actually doing?
- When are my peak usage times?
You can scabble around in the Admin portal and try to piece together the answers, but it is a bit like trying to navigate a city with a paper map — you can get there eventually, but it is slow and painful, and you will probably miss some things along the way.
Read on to see how FUAM can help answer these sorts of questions.
Comments closedYaron Pri Gal announces support for a library:
Today, we’re announcing that fabric‑cicd—the open‑source Python deployment library for Microsoft Fabric—is now an officially supported, Microsoft‑backed tool for CI/CD automation across Fabric workspaces.
Over the past year, fabric‑cicd has rapidly evolved through collaboration with engineering, CAT, MVPs, enterprise customers, and the community. Growing usage, strong sentiment across internal and external channels, and adoption by organizations building enterprise‑grade deployment pipelines helped solidify its value within the Fabric ecosystem.
Read on to learn what this means.
Comments closedRob Sewell shows off a repository:
Today I want to talk about fabric-toolbox, the Microsoft-sponsored, community-driven repository of accelerators, tools, and utilities that exists specifically to help Fabric users get further, faster.
Read on to learn more about what fabric-toolbox is, why it’s interesting, and what Rob’s next blog posts on the topic will cover.
Comments closedReitse Eskens goes back to the well:
For those of you who have been following this blog for a long time, you may have read the posts on Fabric where I’m comparing the F64 Trial with the F2, and other shenanigans. Because Fabric keeps evolving, and new releases keep coming that improve or change the behaviour, I felt it was only fair to give Fabric a new run for its capacities.
The idea is not to create a solution that works as quickly as possible. It’s not the goal to tune Fabric, nor to get the most excitement for your Euro. The main goal of this blog (and the session that I’m presenting on this topic), is to show you the differences, the error messages and where to look when you get lost. Because, for all its intents and purposes, error handling is still tricky, and it seems to be very hard to get rid of “Something went wrong” messages.
I appreciate Reitse’s localization of the well-known phrase “most win for your Yen.”
Click through for plenty of graphs and lots of testing.
Comments closedGilbert Quevauvilliers runs a test:
After my previous blog post on the different semantic model options and at the same time working with a Fabric customer, it got me thinking which is faster and which consumes less capacity when ingesting data into Power BI either via the SQL Endpoint to a Lakehouse or a query from the Warehouse.
Below you will find the information which I found very interesting indeed.
For both the Lakehouse and Warehouse source CSV’s there was a total of 237,245,585 rows.
Click through for the numbers, and a scenario in which the warehouse loads data faster than a lakehouse.
Comments closedNicky van Vreonhoven notices a change in language:
Just a quick post because I noticed a change in the Fabric UI, specifically in the Workspace settings.
I am working on a demo for my Power BI Gebruikersdagen session, and wanted to switch a workspace to Fabric capacity. I noticed that the setting License type has changed, and is now called Workspace type.
Read on to see where this has changed and a few more notes from Nicky.
Comments closedKenneth Omorodion builds a Dataflows Gen2 pipeline:
In a previous tip, I described how we can implement snapshot reporting using Microsoft Fabric Dataflow Gen2. In this article, I will describe how to achieve the same using Microsoft Fabric Pipelines. I previously described how important snapshot reporting can be in Business Intelligence reporting. Some reasons why developers/engineers might prefer to leverage a Fabric pipeline instead of a Dataflow Gen 2 include considerations around cost efficiency and data volumes.
My strong preference is still to do this in code (notebooks, Spark jobs), but at least Dataflows Gen2 aren’t literally 100x slower than the alternatives anymore.
Comments closedRuixin Xu puts together a how-to guide:
Across industries, teams use Power BI to understand what has already happened. Dashboards show trends, highlight performance, and keep organizations aligned around a shared view of the business.
But leaders are asking new questions—not just what happened, but what is likely next and how outcomes might change if they act. They want insights that help teams prioritize, intervene earlier, and focus effort where it matters. This is why many organizations look to enrich Power BI reports with machine learning.
This challenge is especially common in financial services.
Consider a bank that uses Power BI to track customer activity, balances, and service usage. Historical analysis shows that around 20% of customers churn, with churn tied to factors such as customer tenure, product usage, service interactions, and balance changes.
Click through for the architecture example and process. The actual model is a LightGBM model, which is generally fine for two-class classification.
Comments closed