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Category: Microsoft Fabric

Charting Microsoft Fabric Workspace Activity

Sandeep Pawar creates a chart:

Semantic Link Labs v0.8.3 has list_activities method to get the list of all activities in your Fabric tenant. It uses the same Power BI Admin - Get Activity Events API but this API now also includes Fabric activities. Note that this is an Admin API so you need to be a Fabric administrator. Check the API details.

To answer the above question, I will use the admin.list_activity_events , loop over the last 30 days and plot the results by Fabric item type in my personal tenant:

Click through for that code. Though if you’re going to do something similar in your environment, I recommend not using a line chart for this visual, as line charts indicate a flow over time and this is definitely point-in-time categorical data. A bar chart or dot plot would be better in that case.

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Reasons to Migrate from Synapse to Fabric

James Serra has a list:

Many customers ask me about the advantages of moving from Azure Synapse Analytics to Microsoft Fabric. Here’s a breakdown of the standout features that make Fabric an appealing choice:

  • Unified Environment for All Users
    Fabric serves everyone—from report writers and citizen developers to IT engineers—unlike Synapse, which primarily targets IT professionals.
  • Hands-Free Optimization
    Fabric is auto-optimized and fully integrated, allowing most features to perform well without requiring technical adjustments.

I suppose that James is too politic to give what I’d consider the top reason: because there have actually been meaningful updates to Microsoft Fabric in the past year. I’m not sure you can really say the same thing about Azure Synapse Analytics.

The tricky part about this, however, is that–to my knowledge, at least–there’s no clean way to migrate dedicated SQL pools.

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Move Data between Lakehouses and Workspaces in Microsoft Fabric

Gilbert Quevauvilliers performs an exfiltration:

With the new Schema’s in a Lakehouse, it now is possible to read from Lakehouse A (In Workspace A) and write to Lakehouse B (In Workspace B).

Here are more details about the Schema preview: Lakehouse schemas (Preview) – Microsoft Fabric | Microsoft Learn

This opens a whole new world of possibilities.

I also really like the fact that I can simply use the Names, and I do not need to get the actual GUIDS!

For example, I can use the following as shown below which is WorkspaceName.LakehouseName,SchemaName.TableName

Click through to see it in action.

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Fabric List Connections API in Semantic Link Labs

Sandeep Pawar has an update for us:

In you case you missed it, List Connections Admin API is now live in Fabric. It was shipped in Semantic Link Labs v 0.7.4 a few weeks ago but at the time of the release it was still private. This API returns all the connections set up in the tenant and requires admin privileges. I still can’t find documentation on it so wait for the official details. Note that this API is different from item – list connection API which lists connections used by an item.

Read on to see what you can get from it.

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Notes on Bursting and Monitoring in Microsoft Fabric

Joey D’Antoni puts in a lot of work and then coasts for a few hours:

Bursting and smoothing is a concern for a lot of Fabric admins—it’s somewhat unpredictable in terms of how large the initial burst is, and how long the smoothing process takes. One problem that a number of both MVPs and customers have observed, is Fabric effectively being unavailable for things like dashboard refreshes for long periods of time. Microsoft describes this process in docs as the following:

“When a scale factor is over 1, it means that burstable capacity is being used to meet the demands of the workload. It also means that your workload is borrowing capacity units from a future time interval. This is a fundamental concept of Microsoft Fabric called smoothing.

Smoothing offers relief for customers who create sudden spikes during their peak times, while they have a lot of idle capacity that is unused. Smoothing simplifies capacity management by spreading the evaluation of compute to ensure that customer jobs run smoothly and efficiently.”

Read on to learn more about this process and how it can be tricky for administrators to manage.

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Lexing DAX with PyDAX

Sandeep Pawar reviews a DAX lexer:

The power of open-source and GenAI. Klaus Jürgen Folz recently open-sourced the PyDAX library, which parses DAX expressions to extract or remove comments, and identify referenced columns and measures. I used that library to create some demos for myself and then shared the notebook along with instructions with Replit agents to build an app for me.. 15 minutes & 3 prompts later I had a fully functional app. Give it a try : https://daxparser.replit.app/

Read on to learn more, including why I referred to PyDAX as a “lexer” and a few more notes of relevance.

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Finding Mutable and Immutable Properties in Microsoft Fabric Spark

Sandeep Pawar wants to make a change:

Spark properties are divided into mutable and immutable configurations based on whether they can be safely modified during runtime after the spark session is created.

Mutable properties can be changed dynamically using spark.conf.set() without requiring a restart of the Spark application – these typically include performance tuning parameters like shuffle partitions, broadcast thresholds, AQE etc.

Immutable properties, on the other hand, are global configurations that affect core spark behavior and cluster setup and these must be set before/at session initialization as they require a fresh session to take effect.

Read on to see how you can tell which is which.

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T-SQL Notebooks in Microsoft Fabric

Dennes Torres tries out T-SQL notebooks:

T-SQL Notebooks is one of the new features announced during FabCon Europe.

The most distracted could miss the fact this is a new feature at all. Yes, it is. Notebooks were capable to support Spark SQL, but T-SQL is something new.

The main examples being announced are built with data warehouses, but let me confirm and highlight this:

T-SQL Notebooks support lakehouses as well.

There is at least one limitation: DML is not supported with lakehouses.

Saving my rant about lakehouses vs warehouses in Fabric, do read what Dennes has to say about T-SQL notebooks as they exist today.

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Dynamically Running Notebooks across Fabric Lakehouse Environments

Ayman El-Ghazali solves a problem:

A few months ago, an ISV customer approached with a request to have notebooks run across Microsoft Fabric Lakehouse environments dynamically.  Initially the first request was to allow pipelines in Fabric to pass parameters for file paths to help with data ingestion.  This would allow the customer to use the same notebook across Lakehouse environments for the customers that they are serving. After resolving this, the scope increased to include the notebook execution. The notebooks should be able to run across workspace environments and not have to be attached to a Lakehouse at the time of execution.  The solution presented below allows for the customer to run notebooks across environments but also allows them to run SQL queries against existing Lakehouse tables; additionally it allows for access to tables created during the notebook execution run without the notebook being attached to the Lakehouse. 

Read on to learn how.

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KQLMagic in Fabric Runtime 1.3

Sandeep Pawar spreads the news:

I wrote a blog last year on the usefulness of KQLMagic command in Fabric notebook and made a suggestion that it should be part of the default runtime. Well, guess what – it’s now in the Fabric Runtime 1.3. No installation necessary and authentication is handled automatically.

Read on to learn more about how you can use KQLMagic in a Microsoft Fabric notebook to read from an Eventhouse.

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