🚀 Build a Fabric Data Agent in Minutes with GitHub Copilot Agent Mode

Discover how to supercharge your data workflows using GitHub Copilot Agent Mode in VS Code. Learn how to explore schemas, generate AI instructions, and create example queries—all in under an hour. If you're working with Microsoft Fabric, this fast and intuitive method is a game-changer for building conversational analytics agents.

What happens when a person leaves organisation or project who had created fabric items and how to take ownership

Many releases and tools within a single platform are engaging both technical users (data engineers, data scientists, or data analysts) as well as end users. Fabric brought a unification of stakeholders into one shared space. That said, it doesn’t mean we have to use all the tools it offers.

If we already have an excellent data cleaning, transformation, or processing workflow using the very popular Databricks, we can keep using it. Fabric can be adopted or integrated in many ways.

Fabric brings us a next-generation lake storage system using an open data format. This means it allows us to use the most popular data file types for storage, and its file system works with conventional open-source structures. In other words, we can connect to our storage using tools capable of reading from it. We've also shown a bit about Fabric Notebooks and how they enhance the development experience.

In this simple tip, we’ll look at how to read from and write to our Fabric Lakehouse using Databricks.

Resume your capacity ➜ Run pipelines ➜ Refresh Power BI datasets ➜ Suspend capacity — all automated thanks to PowerShell and Azure Automation ! 

Microsoft Fabric revolutionizes data architecture by offering a unified platform that integrates Power BI, data science, real-time analytics, and more. At the heart of this ecosystem is the Lakehouse, a powerful, flexible, and scalable storage layer tailored for modern data engineering workflows.

In this article, we explore how Lakehouses work in Microsoft Fabric, how to set one up, and how they serve as the foundation for managing both files and structured data—all without the traditional complexity of data platforms.

In the ever-evolving landscape of data management, optimizing storage and access times is paramount. This article delves into the innovative V-Order feature in Fabric, a game-changer for data read times and storage efficiency. Discover how V-Order's write-time optimization technique enhances performance, reduces costs, and transforms data operations. Join us as we explore the technical intricacies, benefits, and real-world applications of V-Order, and learn how it can revolutionize your data management strategies.

Learn how to connect cross Tenant Azure Data Factory (and other services) to Fabric.

There has been a data cleaning extension on the market for a while now that continues to attract attention. I’ve typically come across two types of profiles who clean data: those who love code (using Python or R) and those who use BI tools (Power BI, Tableau, etc.). I believe this extension aims to integrate the best of both worlds—using the power of Python with the visual convenience of traditional tools.

This article tells us about Data Wrangler. The extension that allows you to perform data transformations from a Python or Jupyter file with clicks, as if it were a BI tool.

Simplify analytics with Spark Connector for Microsoft Fabric Data Warehouse: seamlessly access Fabric Warehouse via a secure Spark API.

In my last article of this series, I covered on how to load csv files in a lakehouse automatically in MS Fabric. In this article, I am going to discuss how we can find and fix errors with notebooks easily. 

OneLake is a unified storage system in Microsoft Fabric that eliminates data silos by storing all data in a single location. Now, we’re going to discuss Direct Lake, a new way Power BI interacts with this storage for faster performance and efficiency.

Source: https://learn.microsoft.com/en-us/fabric/fundamentals/direct-lake-overview

The dynamic landscape of modern business demands more than just data-driven insights; it requires a seamless and adaptive approach to business intelligence (BI) adoption. While advanced tools like Microsoft Fabric and Power BI have revolutionized data analytics, many organizations struggle to translate their potential into impactful decision-making. This article outlines actionable strategies to simplify BI adoption, emphasizing the importance of aligning key performance indicators (KPIs), fostering agile practices, enhancing data quality, and leveraging advanced visualizations. By integrating these principles, supported by Microsoft’s ecosystem, businesses can transform their BI capabilities into a competitive advantage.

To achieve a seamless BI adoption, organizations must focus on aligning KPIs with evolving business objectives while ensuring they remain measurable and actionable. Agile BI practices, including iterative development and user feedback, are crucial to maintaining relevance in a fast-changing environment. Robust data governance, effective integration of diverse sources, and consistent validation are essential for ensuring data quality and trust. Employing advanced visualization techniques tailored to user needs enhances clarity and engagement, while automating workflows minimizes errors and saves time.

User training and support play a pivotal role in fostering a culture of data literacy, ensuring employees can confidently navigate tools like Microsoft Fabric and Power BI. Monitoring user engagement and incorporating feedback ensures reports evolve in alignment with user needs. Measuring the impact of BI on business outcomes validates its strategic value, while collaboration between IT and business teams ensures a holistic approach. Finally, full utilization of Microsoft’s tools, including Power BI and Azure OpenAI, maximizes the transformative potential of BI systems. Together, these elements drive continuous improvement and enable organizations to achieve excellence in data-driven decision-making.

The dynamic landscape of modern business demands more than just data-driven insights; it requires a seamless and adaptive approach to business intelligence (BI) adoption. While advanced tools like Microsoft Fabric and Power BI have revolutionized data analytics, many organizations struggle to translate their potential into impactful decision-making. This article outlines actionable strategies to simplify BI adoption, emphasizing the importance of aligning key performance indicators (KPIs), fostering agile practices, enhancing data quality, and leveraging advanced visualizations. By integrating these principles, supported by Microsoft’s ecosystem, businesses can transform their BI capabilities into a competitive advantage.

To achieve a seamless BI adoption, organizations must focus on aligning KPIs with evolving business objectives while ensuring they remain measurable and actionable. Agile BI practices, including iterative development and user feedback, are crucial to maintaining relevance in a fast-changing environment. Robust data governance, effective integration of diverse sources, and consistent validation are essential for ensuring data quality and trust. Employing advanced visualization techniques tailored to user needs enhances clarity and engagement, while automating workflows minimizes errors and saves time.

User training and support play a pivotal role in fostering a culture of data literacy, ensuring employees can confidently navigate tools like Microsoft Fabric and Power BI. Monitoring user engagement and incorporating feedback ensures reports evolve in alignment with user needs. Measuring the impact of BI on business outcomes validates its strategic value, while collaboration between IT and business teams ensures a holistic approach. Finally, full utilization of Microsoft’s tools, including Power BI and Azure OpenAI, maximizes the transformative potential of BI systems. Together, these elements drive continuous improvement and enable organizations to achieve excellence in data-driven decision-making.

The dynamic landscape of modern business demands more than just data-driven insights; it requires a seamless and adaptive approach to business intelligence (BI) adoption. While advanced tools like Microsoft Fabric and Power BI have revolutionized data analytics, many organizations struggle to translate their potential into impactful decision-making. This article outlines actionable strategies to simplify BI adoption, emphasizing the importance of aligning key performance indicators (KPIs), fostering agile practices, enhancing data quality, and leveraging advanced visualizations. By integrating these principles, supported by Microsoft’s ecosystem, businesses can transform their BI capabilities into a competitive advantage.

To achieve a seamless BI adoption, organizations must focus on aligning KPIs with evolving business objectives while ensuring they remain measurable and actionable. Agile BI practices, including iterative development and user feedback, are crucial to maintaining relevance in a fast-changing environment. Robust data governance, effective integration of diverse sources, and consistent validation are essential for ensuring data quality and trust. Employing advanced visualization techniques tailored to user needs enhances clarity and engagement, while automating workflows minimizes errors and saves time.

User training and support play a pivotal role in fostering a culture of data literacy, ensuring employees can confidently navigate tools like Microsoft Fabric and Power BI. Monitoring user engagement and incorporating feedback ensures reports evolve in alignment with user needs. Measuring the impact of BI on business outcomes validates its strategic value, while collaboration between IT and business teams ensures a holistic approach. Finally, full utilization of Microsoft’s tools, including Power BI and Azure OpenAI, maximizes the transformative potential of BI systems. Together, these elements drive continuous improvement and enable organizations to achieve excellence in data-driven decision-making.

A Guide to Working with Lakehouses in Microsoft Fabric

This guide explores the data engineering experience in Microsoft Fabric, focusing specifically on Lakehouses. This guide takes a hands-on approach, demonstrating how to work with Lakehouses and manage data effectively.

Downloadable Files 

In my last newsletter on LinkedIn, I explained how to export AdventureWorks2022 tables to csv files. If you don’t want to generate them, you can get them, as stated in the last post (after my edit in which I realized I made a mistake). My actual blog kayondata.com is currently being moved to another place, hence not updated at the moment.

CSV files are still very common, so I am using this approach to showcase some tricks and to exercise some data engineering stuff.