This article is based on official Microsoft Fabric documentation and practical learning resources provided by Microsoft. To move beyond theory and demonstrate real implementation, I also followed a hands-on Lakehouse lab published by Microsoft Learning. The lab walks through core concepts such as creating a lakehouse, ingesting data, and exploring it using different Fabric experiences.

If you would like to explore the same step-by-step exercise used in this article and in my demonstration, you can access the lab here:

Lab 

This article explains how Microsoft Fabric integrates with Moodle LMS REST API to create a scalable and reliable learning analytics ecosystem. We will walk through API integration, ingestion, lakehouse storage, Spark optimization, and automated pipelines: the foundation required to operationalize LMS analytics at an enterprise level.

Scrape currency data from Hurriyet/Doviz with Python BeautifulSoup and store it in Microsoft Fabric Lakehouse or Warehouse step by step.

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.

💡Ever wondered how to bring AI into your data engineering workflows in Microsoft Fabric?

In my latest hands-on project, I show how to automate Sentiment Analysis on customer feedback using:

• Microsoft Fabric Lakehouse

• PySpark notebooks

• Azure OpenAI (GPT-4)

• Fabric Data Pipelines

• Power BI for real-time insights

This solution is fully integrated, no external services required, and takes just 10 minutes to set up.

📖Check out the full blog and video tutorial to see it in action: 

#MicrosoftFabric #AzureOpenAI #DataEngineering #SentimentAnalysis #PowerBI #PySpark

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.

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.