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Category: Python

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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Prompt Flow in Azure AI

Published 2024-12-24 by Kevin Feasel

Tomaz Kastrun continues a series on Azure AI. First up is an introduction to Prompt Flow:

Prompt flow in Azure AI Foundry is development tool for designing the flows (streamlines) for the complete end-to-end development cycle of LLM’s AI application. You can create, iterate, test, orchestrate, debug, and monitor your flows.

After that, we get a demonstration a Prompt Flow in Python:

Prompty gives you the ability to create an end-to-end solution, like RAG where you can chat with LLM over an article or document, where you can ask to classify the input data (list of URLs,…)

Prompty is a markdown file, structured in YAML and encapsulates a series of metadata fields pivotal for defining the model’s configuration and the inputs. After this front matter is the prompt template, articulated in the Jinja format.

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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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Building and Deploying a Streamlit Data App

Published 2024-12-17 by Kevin Feasel

Ivan Palomares Carrascosa deploys an app:

This article will navigate you through the deployment of a simple machine learning (ML) for regression using Streamlit. This novel platform streamlines and simplifies deploying artifacts like ML systems as Web services.

I’ll leave aside my aside that linear regression isn’t machine learning. Click through to see how you can build a simple application in approximately 60 lines of code. This example shows off some of the simplicity in Streamlit’s design.

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Churn Analysis using Logistic Regression in Python

Published 2024-12-13 by Kevin Feasel

Daniel Calbimonte takes us through a churn analysis scenario:

This article explains how to analyze the data using Python and perform customer churn analysis to determine why customers stop using a service.

Read on for the article. If you want to dig deeper into churn analysis, I can recommend a book entitled Fighting Churn with Data. Its focus is more on categorical and numerical analysis rather than using statistical classification techniques like logistic regression to identify churn factors. That makes it easier to digest for non-statisticians, especially because most of the code is SQL.

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Using the Azure AI Language and Translation Python SDK

Published 2024-12-12 by Kevin Feasel

Tomaz Kastrun continues a series on Azure AI:

Using SDK options for “Language + Translation” service is

pip install azure-ai-textanalytics==5.2.0

and adding your endpoint in format like: https://yyyyy_azurehub_xxxxxxx.cognitiveservices.azure.com/

and secret to your endpoint. And you will also need the region name (e.g.: west-europe).

Once you’ve set up the necessary credentials, Tomaz shows how easy it is to call the service.

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Using the Azure AI Speech Python SDK

Published 2024-12-10 by Kevin Feasel

Tomaz Kastrun writes some code:

Besides Python Speech SKD there are multiple languages supported with Speech SDK. Python SDK will expose you many of the Speech service capabilities for developing speech-enabled applications. Ideal for scenarios for (near) real-time and non real-time cases by using other Azure services as storage, streams and analytics

Click through for a demonstration.

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API Testing with pytest

Published 2024-12-09 by Kevin Feasel

Xuan Nguyen Truong writes some tests:

API testing is an essential aspect of software development, ensuring that your application’s endpoints are functioning correctly and reliably. In this guide, we’ll introduce you to implement API testing in Python with Pytest and the Requests library.

I’m a big fan of pytest, as it makes testing in Python so much easier. There’s not a lot of ceremony involved in writing tests and it’s easy to see what’s failing during tests.

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Delta Tables in Microsoft Fabric with Polars

Published 2024-12-04 by Kevin Feasel

Sandeep Pawar tries out the Polars library:

The much-anticipated Python notebook in Fabric is finally available and the Fabric users have already developed cool libraries and blogged about the usefulness of these notebooks. Duckdb is everyone’s favorite, but I am a Python guy so here is quick overview of how you can use Polars in the Python notebook.

Polars is an open-source library that uses a Rust engine and supports multi-threaded execution. This means it’s significantly faster than pandas and, in some cases, even faster than Spark. It can efficiently use the limited resources available in Python notebooks (2 cores, 16GB RAM). Polars v1.6 is installed in the default Python notebook environment. So, let’s see how to perform some common operations.

Read on to see how you can load and write out files via Polars.

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