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Category: Notebooks

Writing Data to an Unattached Lakehouse via Fabric Notebook

Published 2024-10-16 by Kevin Feasel

Prathy Kamasani does a bit of movement:

Regardless of which architecture we follow, during stages of data integration and transformation there’s always a step to move data from one location to another. And, we work with multiple tables, schemas, and even lake houses.Same goes with Fabric Notebooks. I often find myself in scenarios where I don’t want to attach Lakehouse to my notebook, but I do want to read or write data from various bakehouses.

I recently blogged about a way to achieve this as part of documenting your workspaces. In that post, I described how to write data to a workspace that was not attached to the notebook. I used MsSparkUtil(renamed to NotebookUtils) to mount and then write data in the Lakehouse as Delta tables.

Read on for the answer.

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Tips for Orchestrating Fabric Notebooks

Published 2024-10-14 by Kevin Feasel

Stepan Resl talks orchestration:

Let’s start by introducing what orchestration is and why it’s important to talk about shared resources. Orchestration is a discipline focused on managing and coordinating individual items or control elements to collectively manage the flow of our data operations. In the context of Fabric, this involves managing notebooks, dataflows, pipelines, stored procedures, semantic model updates, and many other items, activities, and services that may even be outside of Fabric.

Read on for some of the options, how they work in Microsoft Fabric, and tips for success.

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Exploring Semantic Model Relationships with Sempy

Published 2024-10-09 by Kevin Feasel

Prathy Kamasani builds a graph:

Understanding the relationships between datasets is crucial in data analytics, especially in the world of self-service BI. Sempy, a Python library unique to Microsoft Fabric, allows users to visualise these relationships seamlessly. This post explores using Sempy to visualise semantic model relationships and view them in a Power BI Report. Viewing them in Notebook is easy and has been documented on MS Docs.

Click through for a notebook and explanation of the underlying code.

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Tracking Microsoft Fabric Notebook Progress

Published 2024-09-26 by Kevin Feasel

Gilbert Quevauvilliers asks are we there yet? are we there yet?

How to view or track the progress of Notebook while it is running in Microsoft Fabric

I was recently working with a Notebook in Microsoft Fabric that was started via a Data Pipeline.

The challenge I had was that I had no idea how far the notebook had gone (as there were quite a lot of cells in this particular notebook).

In this blog post I am going to show you how I can use Microsoft Fabric to identify exactly which cell my notebook is currently on.

Click through for the answer. And so help me, if you ask that question one more time, I’m turning this thing around and we’re going back home.

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Updating the Default Lakehouse of a Notebook

Published 2024-09-20 by Kevin Feasel

Sandeep Pawar makes a change:

I have written about default lakehouse of a Fabric notebook before here and here. However, unless you used the notebook API, there was no easy/quick way of removing all/selective lakehouses or updating the default lakehouse of a notebook. But thanks to tip from Yi Lin from Notebooks product team, notebookutils.notebook.updateDefinition has two extra parameters, defaultLakehouse and defaultLakehouseWorkspace which can be used to update the default lakehouse of a notebook. You can also use it to update environment attached to a notebook. Below are some scenarios how it can be used.

Click through for those scenarios.

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Querying a Fabric SQL Endpoint via Notebook and T-SQL

Published 2024-09-06 by Kevin Feasel

Sandeep Pawar talks about a Spark connector:

I am not sharing anything new. The spark data warehouse connector has been available for a couple months now. It had some bugs, but it seems to be stable now. This connector allows you to query the lakehouse or warehouse endpoint in the Fabric notebook using spark. You can read the documentation for details but below is a quick pattern that you may find handy.

Despite it not being anything new, it is still interesting to see the use case of writing T-SQL instead of Spark SQL.

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mssparkutils now notebookutils and Validating DAGs in Fabric

Published 2024-08-26 by Kevin Feasel

Sandeep Pawar gives us two quick hits:

First, if you haven’t noticed mssparkutils has been officially renamed to notebookutils. Check out the official documentation for details. Be sure to use/update your notebooks to notebookutils.

Read on for a pair of notes around this name change, as well as some capabilities to validate DAGs when using runMultiple to orchestrate multiple notebook executions.

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Databricks Notebook Package Installation and Variables

Published 2024-08-26 by Kevin Feasel

Chen Hirsh diagnoses a problem:

A friend called to ask for my help with a weird issue. In a Databricks notebook using Python, he declares and assigns a variable in the first cell. Something like that:

my_var = 1

He then runs the rest of the notebook, and somewhere along the way, tries to use this variable, and gets this message:

NameError: name 'my_var' is not defined

Going back to cell 1, and checking the value of my_var, he gets the same error.

Read on for the root cause of the issue, as well as a pair of helpful tips from Chen.

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Downloading Power Automate Scanner API Data into a Notebook

Published 2024-08-21 by Kevin Feasel

Gilbert Quevauvilliers creates a notebook:

I was recently working with a customer where they had more then 100 app workspaces and I was running into some challenges when using the Scanner API in Power Automate.

I then discovered this blog post where they detailed how to download the Scanner API data (DataXbi – admin-scan.py), it was not quite in the format that I needed, so below is my modified code.

The reason that I am downloading the Scanner API into a JSON file is that I find it easier to extract the data that I need using Power BI Desktop.

Click through for the code and how it all works.

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