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Category: Spark

Checking Key Vault Access in Microsoft Fabric Spark Notebooks

Published 2025-05-09 by Kevin Feasel

Marc Lelijveld has clearance:

Working with sensitive data in Microsoft Fabric requires careful handling of secrets, especially when collaborating externally. In a recent customer engagement, I needed to validate access to Azure Key Vault from within a Fabric Notebook, without ever exposing the actual secret values. With only read access granted and no need to manage or update secrets, I focused on confirming that the connection was working as expected.

In this blog, I’ll walk you through the approach, including the setup, code snippets, and logic behind this quick but crucial verification step.

Click through for the full story.

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When to Use a Python Notebook vs Spark Notebook in Microsoft Fabric

Published 2025-04-23 by Kevin Feasel

Gilbert Quevauvilliers lays out the plan:

This is the first blog post in a series of blog posts where I dive into how to use Python notebooks instead of Spark notebooks. For example, I will show you how to run a SQL query from a Lakehouse table and get it into a data frame. Read and write to a Lakehouse table and more.

NOTE: This is still in preview, but I personally think that this is worth investing time in learning.

The reason I am using the term Python is because the notebook can ONLY use Python and not any of the other languages available in a Spark

Also, in fairness, I’ve heard people working on Microsoft Fabric within the company reference these as ‘Python notebooks,’ so Gilbert is in good company.

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Customizing Spark Settings in Microsoft Fabric Workspaces

Published 2025-04-11 by Kevin Feasel

Nikola Ilic doesn’t accept the default:

In this article, I’ll walk you through how to go from out-of-the-box default Spark configurations to a fine-tuned setup that suits your specific workloads and requirements, as well as getting you ready for the DP-700 exam.

Spark is an extremely powerful engine, but like any powerful tool, it runs best when you tune it. So, don’t always settle for default. Get dynamic—and get Spark working the way you need it to.

Click through for the explanation of functionality.

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Common Data Model Connector for Synapse Spark 3.4

Published 2025-04-08 by Kevin Feasel

Richard Swinbank deals with a totally-not-deprecated platform:

The underlying problem here appears to be that the Spark connector is simply not supported in v3.4. There’s very little I can find to officially confirm or deny this, but an answer to this question on Microsoft Q&A backs this up. The answer also suggests a few options, including:

  1. downgrade to Spark 3.3 – this isn’t an option because it’s end-of-life
  2. migrate to Fabric – long term this is good idea, but it’s not a quick fix for this problem.
  3. use alternative data access methods, e.g. using the Azure Data Lake Storage Gen2 connector.

In this article, I take a look at option (3).

Click through for Richard’s workaround.

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Data Quality Management with Great Expectations and Databricks

Published 2025-03-27 by Kevin Feasel

Sairamakrishna BuchiReddy Karri and Srinivasarao Rayankula show off Great Expectations:

Data quality checks are critical for any production pipeline. While there are many ways to implement them, the Great Expectations library is a popular one. 

Great Expectations is a powerful tool for maintaining data quality by defining, managing, and validating expectations for your data. In this article, we will discuss how you can use it to ensure data quality in your data pipelines.

Click through to see how it all works.

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Writing Data into a Microsoft Fabric Lakehouse via Notebook

Published 2025-03-12 by Kevin Feasel

Stepan Resl writes some code:

Since Lakehouse is one of the key items within Microsoft Fabric, it is important to know how to write data into it in various formats and using different tools. One of the most common tools is notebooks, as they provide great flexibility and speed for development and testing with graphical outputs. In this article, I want to focus primarily on the following types of notebooks:

  • PySpark
  • Python

Click through to see how it works in both notebook types.

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Microsoft Fabric Shortcuts and Lakehouse Maintenance

Published 2025-03-07 by Kevin Feasel

Dennes Torres has a public service announcement:

I wrote about lakehouse maintenance before, about multiple lakehouse maintenances, published videos about this subject and provided sample code about it.

However, there is one problem: All the maintenance execution should be avoided over shortcuts.

The tables require maintenance in their original place. According to our solution advances, we start using shortcuts, lots of them. Our maintenance code should always skip shortcuts and make the maintenance only on the tables.

Click through to see how you can differentiate shortcuts from actual tables and write code to avoid shortcuts.

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Spark Connector for Fabric Data Warehouse

Published 2025-02-28 by Kevin Feasel

Arshad Ali announces a connector:

We are pleased to announce the availability of the Fabric Spark connector for Fabric Data Warehouse (DW) in the Fabric Spark runtime. This connector enables Spark developers and data scientists to access and work with data from Fabric DW and the SQL analytics endpoint of the lakehouse, either within the same workspace or across different workspaces, using a simplified Spark API. The connector will be included as a default library within the Fabric Runtime, eliminating the need for separate installation.

Click through to check out its capabilities. This is a tiny step toward where I think Microsoft Fabric should go: any tool accessing the same data, eliminating separate lakehouses vs warehouses and having to remember that you can’t use this syntax in this scenario unless you connect to it this way and sacrifice one live chicken.

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Table Compaction in Apache Spark

Published 2025-02-27 by Kevin Feasel

Miles Cole groups things together:

If there anything that data engineers agree about, it’s that table compaction is important. Often one of the first big lessons that folks will learn early on is that not compacting tables can present serious performance issues: you’ve gotten your lakehouse pilot approved and it’s been running for a couple months in production and you find that both reads and writes are increasingly getting slower and slower while your data volumes have not increased drastically. Guess what, you almost surely have a “small file problem”.

What engineers won’t always sing the same tune on is how and when to perform table compaction.

Read on for a dive into the power of compaction (converting a large number of small files into a small number of large files) and plenty of tips along the way.

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Object Ownership in Databricks

Published 2025-02-03 by Kevin Feasel

Chen Hirsh shares a tale of woe:

Have you ever made a change in your system and immediately regretted it? A few weeks ago, I did just that while working with a customer on their Databricks platform. His IT guys made some changes, moving a user to another domain. In Databrick, this is considered a new user, so I added the new user and gave him the same permissions as the old user.

And then, without thinking twice, I deleted the old user from Databricks.

Things did not go well from there. Read on to learn what happened, why, and how to avoid this problem in the future.

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