A Fine Slice Of SQL Server
Jared 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 closedTwinkle Cyril gets something GA:
Introducing distributed session-scoped temporary (#temp) tables in Fabric Data Warehouse and Fabric Lakehouse SQL Endpoints.
#temp tables have been a feature of Microsoft SQL Server (and other database systems) for many years. In the current implementation of Fabric data warehouse, #temp tables are session scoped or local temp tables. Global temp tables are not included in this release.
Session-scoped #temp tables exist only within the session in which they are created and last only for the duration of that session. They are not visible to other users or sessions and are automatically dropped from the system once the session ends or the user decides to drop the temp table. These tables are accessible to all users without requiring specific artifact-level permission.
Click through for examples of how it works and how you can specify a session-level temp table over a local temp table.
Comments closedReitse Eskens shows off Microsoft Fabric’s Monitor hub:
When we create a data solution for our company or clients, we create the best, right? Sure, we do. But still, monitoring the solution is something you should never forget. Your solution can be stable, but the data usually isn’t. And it just feels good to know all the data has been processed correctly.
So, let’s dig into monitoring Microsoft Fabric.
Read on to see how it works, as well as plenty of thoughts, advice, and critique from Reitse.
Comments closedEvanjalin Joseph lays out a comparison:
Take ShopSmart, a global retail chain that operates both online and offline. The company wants to combine its sales, inventory, and customer data in order to facilitate real-time reporting and predictive analytics. Two top platforms are being assessed by the IT team for this change.
Azure, Power BI, and Microsoft 365 are already widely used by ShopSmart, which is in line with Fabric’s integrated ecosystem. The alternative, however, provides more multi-cloud flexibility and strong performance on structured data. The group has to choose between selecting a more specialized warehousing solution with more deployment options or making use of its current Microsoft investments.
Let’s examine the differences between the two platforms.
Click through for an overview of each platform and how they stack up against one another.
Comments closedWhen planning a Data Platform for your organization on Microsoft Fabric, you need to consider workspaces during your design process. Proper workspace design is critical for the organization and consumption of Fabric items. Understanding how to effectively manage Microsoft Fabric workspaces can streamline your processes.
If your workspaces are not set up in a way that aligns with functional business verticals or environments (such as Dev/UAT/Prod), you will end up spending a significant amount of time and effort re-factoring this technical debt to meet the desired organizational structures. While it seems trivial to simply move a report, pipeline, or other workload from one workspace to another, the inbuilt dependencies can often be complex. With efficient planning and design efforts, these problems can be avoided.
Click through for Ron’s advice.
Comments closedPower BI provided a wide range of notification options, each with its own advantages and disadvantages, often requiring us to combine them to achieve the desired result. Historically, we primarily relied on Subscriptions, Alerts, Outages Emails, and Refresh Error Emails. Combining these options was complex and inefficient. With the introduction of the ExecuteQuery endpoint in the Power BI REST API, entirely new possibilities opened up. By sending a DAX query to a selected dataset and processing the returned data, we can create virtually any custom notification, enabling data-driven notifications. This endpoint also came with built-in support for Microsoft Power Automate and Logic Apps, significantly simplifying the process due to their native connectors to other services like Exchange, SharePoint, and Microsoft Teams (including support for Adaptive Cards).
Read on for a bit of history, why a custom solution was necessary for Stepan, and the process of building out that solution.
Comments closedJovan Popovic makes an announcement:
The BULK INSERT statement is generally available in Fabric Data Warehouse. The
BULK INSERTstatement enables you to ingest parquet or csv data into a table from the specified file stored in Azure Data Lake or Azure Blob storage:BULK INSERT table_name FROM file_url_path
Read on for some additional notes around bulk inserting into Fabric Data Warehouses.
Comments closed