A Fine Slice Of SQL Server
Koen Verbeeck answers a question:
We’re a relatively small shop that has been using Power BI for our analytical needs for years now. We’re very pleased with the product, but the recent introduction of Microsoft Fabric has made us a bit anxious. When comparing Microsoft Fabric vs Power BI, it all seems very complex and we’re not even sure we need it. What will happen with Power BI? Will it be replaced with Fabric?
Click through for Koen’s advice and thoughts on the matter.
Comments closedPatrick LeBlanc is back with another compendium:
Welcome to the Fabric April 2025 Feature Summary! This update brings exciting advancements across various workloads, including Low-code AI tools to accelerate productivity in notebooks (Preview), session Scoped distributed #temp table in Fabric Data Warehouse (Generally Available) and the Migration assistant for Fabric Data Warehouse (Preview) to simplify your migration experience.
This one isn’t quite as long as last month’s release, but there are still a couple dozen entries.
Comments closedAndy Leonard doesn’t need to ask for permission:
The problem we are trying to solve is: Grant an Azure Data Factory permission to execute the ReST API method calls against artifacts in a Fabric workspace.
I begin visiting the Azure Portal. If you don’t have an Azure account, you can create one. You can do a lot of things in Azure for little money, but you need an account and that account needs to be secured by a working source of funds in case you leave a virtual machine running for days. Hypothetically. BE CAREFUL. DO NOT LEAVE A VM RUNNING FOR DAYS.
Good en passant advice. I once blew through a $250 Azure credit by laving an HDInsight cluster on for, uh, a few hours.
Comments closedGilbert Quevauvilliers writes a SQL statement:
I come from a TSQL background, so using SQL makes it easy for me to work with data.
There are multiple ways to use SQL in a PySpark notebook, and when I started using a Python notebook it was not so straightforward.
In this blog post I will show you how I use SQL Code.
As mentioned previously I am by no means an expert, I typically find a way that works, is fast and doesn’t consume a lot of capacity. If that works consistently for me then that is how I go about it.
Click through for the solution, which uses DuckDB. As such, the SQL syntax isn’t T-SQL—it’s more like psql. But it does do a great job of interacting with Parquet files and Delta tables.
Comments closedJon Vöge performs a migration:
So your old Power BI Premium Capacity has run/is running out, and your organization is acquiring a new Fabric Capacity to replace it.
Perhaps the organization even decided to take the chance to move the capacity region to something a little closer to home?
If you find yourself in this situation, how do you best migrate your contents of one Capacity to another?
Read on as Jon explains the migration process within a region (which is very easy) and the migration process if you need to go cross-region (which is rather cumbersome).
Comments closedKoen Verbeeck leaves the confines of Microsoft Fabric:
In the blog post Call a Fabric REST API from Azure Data Factory I explained how you can call a Fabric REST API endpoint from Azure Data Factory (or Synapse if you will). Let’s go a step further and execute a Fabric Data Pipeline from an ADF pipeline, which is a common request. A Fabric capacity cannot auto-resume, so you typically have an ADF pipeline that starts the Fabric capacity. After the capacity is started, you want to kick-off your ETL pipelines in Fabric and now you can do this from ADF as well.
Click through for the process. Though do check the warnings that Koen offers around either spending extra money by remaining in synchronous execution mode, or always getting a positive result in asynchronous execution mode, regardless of whether the underlying Fabric Data Pipeline worked or not.
Comments closedJared Westover runs into a wall:
I was excited when Microsoft announced the ability to create session-scoped temporary tables in a Fabric warehouse. However, after using Microsoft Fabric temporary tables, I quickly felt disappointed. When will they be ready for prime time, and in the meantime, what other options are available?
Click through for Jared’s experience, although it might already be fixed.
Comments closedSakshi Jain announces a change:
Previously, there were no restrictions on the number of Fabric items that could be created in a workspace, with a limit for Power BI items already being enforced. Even though this allows flexibility for our users, having too many items in workspaces reduces the overall user friendliness and effectiveness of the platform.
As of April 10, 2025, Microsoft Fabric has implemented updates to the total number of items permissible in a workspace. This change introduces a combined limit of 1,000 Fabric items (including Power BI items) per workspace. In other words, a workspace may now contain up to 1,000 items from both Fabric and Power BI collectively.
This improves usability of the workspace and simplifies organization of Fabric items. This also improves service quality and reliability for users.
Well, that’s one way to spin it.
That limit of 1000 items seems quite restrictive to me, knowing how quickly you can accrue Fabric items.
Comments closedNikola Ilic does a bit of digging:
Before you proceed, in case you don’t know what Direct Lake is, I’ve got you covered in this article, where you can learn and understand various Direct Lake concepts, as well as in which scenarios you might consider implementing Direct Lake semantic models. Now that you know what Direct Lake is, let’s digest the latest news…
A couple of days ago, I was reading the official blog post about the latest enhancement to the Direct Lake storage mode for semantic models in Microsoft Fabric. The official blog post can be found here.
Click through for that announcement and what it means.
Comments closedGilbert Quevauvilliers lays out the plan:
This is the first blog post in a series of blog posts where I dive into how to use Python notebooks instead of Spark notebooks. For example, I will show you how to run a SQL query from a Lakehouse table and get it into a data frame. Read and write to a Lakehouse table and more.
NOTE: This is still in preview, but I personally think that this is worth investing time in learning.
The reason I am using the term Python is because the notebook can ONLY use Python and not any of the other languages available in a Spark
Also, in fairness, I’ve heard people working on Microsoft Fabric within the company reference these as ‘Python notebooks,’ so Gilbert is in good company.
Comments closed