A Fine Slice Of SQL Server
Miles 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 closedTemidayo Omoniyi takes us through an example of creating a microservice:
Today’s architecture is serverless intensive, with multiple microservices performing a particular task. Industries are beginning to move away from traditional monolithic applications, which have a single large codebase infrastructure handling everything, to an easier microservice approach.
Click through for a primer on serverless architecture, microservices, and how to create a simple Python app that acts as a microservice.
Comments closedMichael Mayer does a bit of file conversion:
Conversion from CSV to Parquet in streaming mode? No problem for the two power houses Polars and DuckDB. We can even throw in some data preprocessing steps in-between, like column selection, data filters, or sorts.
This is certainly not the only way to perform the task, though it’s fast and effective.
Comments closedCornelius Yudha Wijaya describes a half-dozen functions:
For many people studying data science, Scikit-Learn is often the first machine learning library they encounter. It’s because Scikit-Learn offers various APIs that are useful for model development while still being easy for beginners to use.
As helpful as they may be, many features from Scikit-Learn are rarely explored and have untapped potential. This article will explore six lesser-known features that will save you time.
The calibration curve function, in particular, drew my attention, especially as I had written that by hand in the past.
Comments closedSince 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.
Comments closedGilbert Quevauvilliers doesn’t need Spark for this:
This blog below explains how to use a Python only notebook to get all the Fabric items using the Fabric REST API.
NOTE: At the time of this blog post Feb 2025, Dataflow Gen2 is not included in the Fabric items, I am sure it will be there in the future.
NOTE II: This only gets the Fabric Items, which does not include the Power BI Items.
Despite the notes, Gilbert leads off with the main reason why you might want to use this: it takes up approximately 5% of the capacity units that a Spark-based notebook does to perform the same operation.
Comments closedMuhammad Asad Iqbal Khan summarizes a document:
Text summarization represents a sophisticated evolution of text generation, requiring a deep understanding of content and context. With encoder-decoder transformer models like DistilBart, you can now create summaries that capture the essence of longer text while maintaining coherence and relevance.
In this tutorial, you’ll discover how to implement text summarization using DistilBart. You’ll learn through practical, executable examples, and by the end of this guide, you’ll understand both the theoretical foundations and hands-on implementation details. After completing this tutorial, you will know:
Click through for the article.
Comments closedVidyasagar Machupalli performs a comparison:
As discussed in my previous article about data architectures emphasizing emerging trends, data processing is one of the key components in the modern data architecture. This article discusses various alternatives to Pandas library for better performance in your data architecture.
Data processing and data analysis are crucial tasks in the field of data science and data engineering. As datasets grow larger and more complex, traditional tools like pandas can struggle with performance and scalability. This has led to the development of several alternative libraries, each designed to address specific challenges in data manipulation and analysis.
This is by no means a comprehensive test, but it does show off quite a few libraries that perform similar actions to Pandas.
Comments closedDennes 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.
Comments closedKevin Chant tries a Python package:
In this post I want to cover my initial tests of fabric-cicd. In order to provide some tips for those looking to work with this new offering.
Just so that everybody is aware, fabric-cicd is a Python library that allows you to perform CI/CD of various Microsoft Fabric items into Microsoft Fabric workspaces. At this moment in time there is a limited number of supported item types. However, that list is increasing.
Read on for the test. It currently supports a limit amount of functionality, but it looks promising.
Comments closed