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Category: Microsoft Fabric

Exploring SQL Databases in Microsoft Fabric

Published 2025-01-13 by Kevin Feasel

Jared Westover looks at the bright side of life:

Over the past few months, I’ve toyed with Microsoft Fabric, focusing on the Data Factory and Power BI experiences. Everything I’ve developed so far is in the proof-of-concept (POC) phase. Naturally, I’m skeptical about new game-changing features, and Fabric is no exception. Any new flashy tech brings bugs along in the early stages. We’ve all been there, working for weeks on a project to have random bugs throw a wrench in everything.

When Microsoft announced SQL databases in Fabric, I was intrigued. After watching the Ignite session, Power AI apps with insights from SQL database in Fabric, a few features instantly stood out, and I want to share my first impressions.

Read on to learn more.

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Finding Capacity-Level Fabric Settings with Semantic Link Labs

Published 2025-01-06 by Kevin Feasel

Sandeep Pawar lists some Microsoft Fabric properties:

Just before the holidays last year Michael Kovalsky released version 0.8.10 of Semantic Links Labs with a bunch of new helpful functions, among them list_server_properties() lists properties of an Analysis Services instance. As you know, in Fabric, the workspace acts as a server which is tied to a capacity. You define these server properties in the Capacity Settings. As far as I am aware, there wasn’t an API to get these capacity settings for audit/monitoring/debugging. With this new function, you can programmatically get the Semantic Model (i.e. Power BI workload) settings.

Click through for an example.

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Microsoft Fabric and Power Platform Resources

Published 2024-12-30 by Kevin Feasel

Jon Voege has a collection of links for us:

This week, to round off the year, we try something different. I wanted to throw a shout out to all the community heroes out there, who also help make the most of Microsoft Fabric, through the use of Microsoft Power Platform (and vice versa).

Also, I wanted to highlight some of their contributions, and hopefully give you all a list of resources to peruse.

Click through for more than 20 links, showing how you can work with Power Automate, Power Apps, Power Pages, and data in Dataverse from Microsoft Fabric.

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Switching between Python and PySpark Notebooks in Fabric

Published 2024-12-23 by Kevin Feasel

Sandeep Pawar wants to save some money:

File this under a test I have been wanting to do for some time. If I am exploring some data in a Fabric notebook using PySpark, can I switch between Python and PySpark engines with minimal code changes in an interactive session? The goal is to use the Python notebook for some exploration or use existing PySpark/SparkSQL or develop the logic in a low compute environment (to save CUs) and scale it in a distributed Spark environment. Understandably, there will be limitations with this approach given the difference in environments, configs etc., but can it be done?

Read on for the answer, as well as plenty of notes around it.

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Scanning Fabric Workspaces via Semantic Link Labs

Published 2024-12-20 by Kevin Feasel

Sandeep Pawar takes us through the Scanner API:

It’s finally here! Thanks to Michael Kovalsky, one of the most requested & anticipated APIs in now available in Semantic Link Labs (v0.8.10) – the Scanner API. The Scanner API in Fabric Admin REST APIs allows Fabric administrators to retrieve detailed metadata about their organization’s Fabric items, supporting governance and compliance efforts. It provides information such as item names, descriptions, date created, lineage, connection strings etc. It’s not new, we have been using it in Power BI for a long time but in the Fabric world, it’s even more important given the number of items and configurations.

Read on to see what’s available and how this works.

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Fabric Benchmarking: Moving CSV Files

Published 2024-12-17 by Kevin Feasel

Eugene Meidinger breaks out the abacus:

First, a disclaimer: I am not a data engineer, and I have never worked with Fabric in a professional capacity. With the announcement of Fabric SQL DBs, there’s been some discussion on whether they are better for Power BI import than Lakehouses. I was hoping to do some tests, but along the way I ended up on an extensive Yak Shaving expedition.

I have likely done some of these tests inefficiently. I have posted as much detail and source code as I can and if there is a better way for any of these, I’m happy to redo the tests and update the results.

Part one focuses on loading CSV files to the files portion of a lakehouse. Future benchmarks look at CSV to delta and PBI imports.

I think Eugene did a fine job documenting everything in the process, and it was interesting to see relative price differences between different techniques for uploading a very large CSV file.

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Sending Alerts from Fabric Workspace Monitoring

Published 2024-12-16 by Kevin Feasel

Chris Webb has a new Bat-signal:

I’ve always been a big fan of using Log Analytics to analyse Power BI engine activity (I’ve blogged about it many times) and so, naturally, I was very happy when the public preview of Fabric Workspace Monitoring was announced – it gives you everything you get from Log Analytics and more, all from the comfort of your own Fabric workspace. Apart from my blog there are lots of example KQL queries out there that you can use with Log Analytics and Workspace Monitoring, for example in this repo or Sandeep Pawar’s recent post. However what is new with Workspace Monitoring is that if you store these queries in a KQL Queryset you can create alerts in Activator, so when something important happens you can be notified of it.

Read on to learn more.

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The Showdown: Spark vs DuckDB vs Polars in Microsoft Fabric

Published 2024-12-13 by Kevin Feasel

Miles Cole puts together a benchmark:

There’s been a lot of excitement lately about single-machine compute engines like DuckDB and Polars. With the recent release of pure Python Notebooks in Microsoft Fabric, the excitement about these lightweight native engines has risen to a new high. Out with Spark and in with the new and cool animal-themed engines— is it time to finally migrate your small and medium workloads off of Spark?

Before writing this blog post, honestly, I couldn’t have answered with anything besides a gut feeling largely based on having a confirmation bias towards Spark. With recent folks in the community posting their own benchmarks highlighting the power of these lightweight engines, I felt it was finally time to pull up my sleeves and explore whether or not I should abandon everything I know and become a DuckDB and/or Polars convert.

Read on for the method and results from several thoughtful tests.

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