A Fine Slice Of SQL Server
Meagan Longoria wants to know who viewed these reports:
Lately I have been building scripts to help clients audit their Fabric environment. The latest request was to get a list of reports and what users have viewed them recently. This is a quick job for a Fabric notebook and the Semantic Link Labs library.
Click through for the notebook, as well as an explanation of how it all works.
Comments closedMarc Lelijveld explains some terms:
It was only yesterday, that I wrote a blog post on Semantic Models as a source for Fabric Data Agents. Not much time has passed, since I learned that Fabric Data Agents does not (always) respect the Synonyms that have been added to a Semantic Model. As a result, the Data Agent may start creating implicit measures, not respecting the definitions and logic in the explicit measures that are part of the Semantic Model.
Long story short, I think we should be able to do better! Therefore, I created a Notebook that helps you to setup Data Agents, collect additional information from your Semantic Model and populate that information automatically as AI notes to the Data Agent.
Read on for the notebook and some additional explanation.
Comments closedGilbert Quevauvilliers continues a series on Python notebooks and DuckDB:
In this blog post I am going to explain how to loop through a data frame to query data and write once to a Lakehouse table.
The example I will use is to loop through a list of dates which I get from my date table, then query an API, append to an existing data frame and finally write once to a Lakehouse table.
Click through for the code, as well as a sample notebook you can use.
Comments closedGilbert Quevauvilliers builds a data frame:
In this blog post I am going to explain how to query multiple Lakehouse tables into a data frame.
The example I am going to use is when you want to load new data into your staging tables, but you need to know the max date from your previous data load.
Read on to see how. The answer, as you might suspect, involves DuckDB.
Comments closedGilbert Quevauvilliers does a bit of writing:
In this blog post I am going to explain how to use a Python Notebook using the Semantic Link module, to run a DAX query and write the output to a Lakehouse table.
I will show you how to install a Python library and then use it within my python notebook.
Read on for a quick primer on Semantic Link Labs, followed by the meat of the article.
Comments closedOlivier Van Steenlandt runs into an error:
As mentioned earlier, I have been playing around with Microsoft Fabric intensively in the past few months. During this period, I ran into a specific issue with one of my notebooks. What happened? Well, I was starting on a new notebook in the evening and life happened… So I stopped playing around to do something else.
A few days later, I wanted to continue my work and remembered that I was required to change something in my data load from a csv file.
Read on for the cause of this error. It’s something that can affect anyone at any time. Even you. Well, probably not you, but the person next to you? Yeah, even that person.
Comments closedWorking 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.
Comments closedGilbert Quevauvilliers does a bit of writing:
When I was exploring how to easily write to Delta Tables with a Python notebook, it took me a considerable amount of time to find out how to do this.
This is my learnings below, and from my point of view it makes it easy to write to a Lakehouse table, like what is done with a PySpark notebook.
Click through for one very important note, as well as the process.
Comments closedMarc Lelijveld provides a public service announcement:
When working with Notebooks in Microsoft Fabric, exporting and reusing them across environments or tenants might seem like a harmless, even convenient, task. Whether you’re sharing a template with a colleague, moving assets between workspaces, or contributing to the community — the last thing you’d expect is to accidentally include data along with your code.
But that’s exactly what can happen.
For people who have worked with Jupyter notebooks in the past, this is a fairly obvious result. But if you aren’t familiar with the platform, that idea may seem weird. Marc does provide some options for exporting notebook contents, and you can also clear the cell contents before exporting.
Comments closedMiles Cole picks up from part one:
This is part 2 of my prior post that continues where I left off. I previously showed how you can use Resource folders in either the Notebook or Environment in Microsoft Fabric to do some pretty agile development of Python modules/libraries.
Now, how exactly can you package up your code to distribute and leverage it across multiple Workspaces or Environment items? How could we acomplish something like the below?
Read on for the answer.
Comments closed