By now, everyone and their mother has heard about AI. It is the greatest technological revolution of our times. And we can do so much with AI, it’s amazing! Today, I want to demonstrate how I use AI copilots for Microsoft Fabric to help me write better code faster. Note: none of the links in …
When you’re using PySpark notebooks in Microsoft Fabric data engineering, you can develop straight from the web browser. While that is interesting, a browser is usually not the most perfect software development environment. In this article I will show you how you can use VS Code Notebooks to develop for Microsoft Fabric. Why Use VS …
Introduction to Delta Lake Liquid Clustering As your Delta tables grow in size, the need for performance tuning in Microsoft Fabric becomes essential. In this post, I’ll explore two powerful optimisation techniques — Delta Lake Partitioning and Liquid Clustering. Both can help improve query speed and reduce costs, but they work in very different ways. …
When managing large-scale data lakes with Microsoft Fabric, performance optimisation becomes crucial. One effective technique to achieve better performance is Delta Lake partitioning. Partitioning can significantly enhance query performance, reduce computational costs, and improve data management efficiency within Microsoft Fabric environments. In this blog post, we will explore what Delta Lake partitioning is, how it …
Understanding the cost implications of implementing Microsoft Fabric is crucial for organisations transitioning to this unified analytics platform. This guide explores the fundamentals of Fabric’s billing model, helping you make informed decisions about resource allocation and cost management. Microsoft Fabric costs is a difficult topic, apparently, let’s make it a bit more understandable! The Basics …
Most APIs these days will have some kind of pagination built into them. This is to make sure that queries against the underlying database are not returning too much data, compromising the database performance as well as sending too large messages across the network. Often, these APIs will tell you in their responses how many …
When we start implementing a data lake using Microsoft Fabric, we might be tempted to start creating pipelines and notebooks right away, without thinking about design principles. However, there’s one design principle I’d like you to consider from the beginning: DRY. DRY is an acronym that stands for Don’t Repeat Yourself. In this blog post, …
Coming from the ‘old school’ world of SSIS and SQL Server, and later Azure Data Factory and Azure SQL Database, I have always built my ETL orchestration processes using some kind of pipelines. In Fabric, we also have pipelines (the successor of ADF), but, we can now also create notebook orchestration using NotebookUtils and runMultiple(). …
In Microsoft Fabric data engineering, we use Spark to apply transformations to our data. Just like you would write T-SQL to transform data in SQL Server, you would write PySpark or SparkSQL to transform data in a Fabric lakehouse. There are a lot of parallels between Spark en SQL in the way you can process …
Introduction In this guide, we’ll dive straight into the practical steps of how to implement Medallion Lakehouses using Microsoft Fabric. If you’re unfamiliar with the Medallion Architecture, or need a refresher, check out our previous post on the theory behind the Medallion Architecture. This post focuses on hands-on implementation, guiding you through each step of …
The medallion architecture is maybe the most important concept to grasp when you start building lakehouse solutions on Microsoft Fabric. Data products should never be built ‘right away’ or on their own. There’s a structure and process to follow. Luckily, we don’t have to figure out that structure by ourselves. Rather, I’d suggest using what …