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Category: Notebooks

Renaming Multiple Columns in a PySpark Notebook

Published 2024-02-29 by Kevin Feasel

Gilbert Quevauvilliers wants one rename to rule them all:

Following on from my previous blog post this blog post I’m going to demonstrate how to bulk rename column names in a single step instead of having to rename them individually.

The reason this came about is because I had a set of data where the column names had the square brackets which I wanted to remove.

As shown below I have highlighted 2 column names with the square brackets.

Read on to see how you can perform somewhat-generic rename operations in Spark notebooks.

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Renaming a Column in Microsoft Fabric via Python Notebook

Published 2024-02-21 by Kevin Feasel

Gilbert Quevauvilliers performs a rename:

I thought it would be good to help others in terms of my learning journey when working with partner notebooks and Microsoft fabric.

In today’s blog post, I am going to show you how to rename a column. In my experience this came up because I had a column name which had a forward slash “/” in it which caused the loading of the data for the table to fail because this is a reserved character.

Read on for the code an example of how it works in action.

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Parallelizing Notebook Runs in Microsoft Fabric via Python

Published 2024-01-30 by Kevin Feasel

Sandeep Pawar kicks off multiple notebooks at once:

The notebook class in mssparkutils has two methods to run notebooks – run and runMultiple . run allows you to trigger a notebook run for one single notebook. Mim wrote a nice blog to show how to use it and its usefulness.

runMultiple , on the other hand, allows you to create a Direct Acyclic Graph (DAG) of notebooks to execute notebooks in parallel and in specified order, similar to a pipeline run except in a notebook.

Read on to learn more about the advantages of this latter approach as well as how you can do it.

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Notebooks versus Dataflow Gen2 in Microsoft Fabric

Published 2024-01-25 by Kevin Feasel

Gilbert Quevauvilliers takes us through a comparison:

In this blog post I am going to compare Dataflow Gen2 vs Notebook in terms of how much it costs for the workload. I will also compare usability as currently the dataflow gen2 has got a lot of built in features which makes it easier to use.

The goal of this blog post is to understand which in my opinion is cheaper and easier to use, which will then be the focus for future blog posts with regards to what I’ve learned along the way, which will hopefully assist you too.

To compare between the two workloads, I am going to be using the same source file as well as do the same transformations which will result in the same result.

Read on for a surprising difference in cost.

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Changing the Width of Cells in Jupyter Notebooks

Published 2024-01-23 by Kevin Feasel

Brendan Tierney blows out the margins:

When working with Jupyter Notebook you might notice the cell width can vary from time to time, and mostly when you use different screens, with different resolutions.

This can make your code appear slightly odd on the screen with only a certain amount being used. You can of into the default settings to change the sizing, but this might not suit in most cases.

It would be good to be able to adjust this dynamically. In such a situation, you can use one of the following options.

Read on for two different ways of doing this.

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Notebook Concurrency in Microsoft Fabric

Published 2023-12-22 by Kevin Feasel

Ed Oldham takes us through a common problem:

If you are currently using Microsoft Fabric you will have some sort of capacity associated with your account. This will have a large impact on what you can run concurrently. If you are on a Fabric Trial, you will have access to a trial capacity and if you are paying you will be on a certain capacity tier based on how much you pay. The following diagram shows information about each level of capacity and the Trial. The Trial resembles F64 capacity but is apparently different in some important ways (More on that later).

Read on to learn more about capacity and what that means for concurrent notebooks and Spark jobs.

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Power BI, Event Streaming, and Notebooks in Microsoft Fabric

Published 2023-12-21 by Kevin Feasel

Tomaz Kastrun continues a series on Microsoft Fabric. Day 18 has us looking at Power BI:

We have created a Power BI report directly from the datalake and today we will check how to do same with dashboard and paginated reports.

Day 19 covers event streaming:

In Fabric, you can create streaming semantic model and when selecting you will get the usual sources:

Day 20 shows how you can work with notebooks in Microsoft Fabric:

Notebooks have been around for a long time and people, community, and professionals have proven the usability, practicality, versioning and reliability of notebooks. Not to mention the clarity and hygiene. But opinions are also divided.

The purpose of this post today is to check for a couple of functionalities that might not be that straightforward when it comes to notebooks.

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Looping through Lakehouses in Microsoft Fabric Spark Jobs

Published 2023-12-20 by Kevin Feasel

Dennes Torres builds a loop:

I have published videos and articles before about Lakehouse maintenance. In this article I want to address a missing point for a lot of Fabric administrators: How to do maintenance on multiple lakehouses that are located in different workspaces.

One of the videos I have published explains the maintenance of multiple lakehouses, but only addresses maintenance in a single workspace. Is it a good idea to keep multiple lakehouses in the same workspace? Probably not.

Click through for the process.

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Creating Charts in Microsoft Fabric Notebooks using Vega

Published 2023-12-14 by Kevin Feasel

Phil Seamark tries out Vega in a Microsoft Fabric notebook:

I recently needed to generate a quick visual inside a Microsoft Fabric notebook. After a little internet searching, I found there are many good quality charting libraries in Python, however it was going to take too long to figure out how to create a very specific type of chart.

This is where Vega came to the rescue. The purpose of this short article is to share a very simple implementation of generating a Vega chart using a Microsoft Fabric notebook.

Click through for the example code.

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