Microsoft Azure and Fabric

Microsoft Fabric and Microsoft Foundry are transforming how developers and data professionals build and manage data-driven applications. When combined with Azure AI Search, these tools create a powerful environment for creating intelligent, scalable search experiences. This post explains how to use Microsoft Fabric and Foundry together with Azure AI Search to build smarter applications that deliver faster, more relevant results. Data flow diagrams displayed on digital screens

The Fabric Data Agent is a conversational AI feature in Microsoft Fabric that lets users ask natural language questions about organizational data and get accurate answers from a coworker who actually reads documentation. It works across data in lakehouses, warehouses, Power BI semantic models, KQL databases, and Fabric ontologies. The mission is to make data exploration simple because SQL is brilliant but not everyone wants to write a CTE during a meeting. To use it, you need

Disclaimer: At the date of this post, IQ and Ontology are still in preview, and this post reflects the current capabilities available for Azure Commercial environments. When a preview feature is delegated, it can be enabled by a capacity admin for that capacity. Working with enterprise data can feel complicated, so it helps when a platform like Microsoft Fabric IQ Preview brings clarity by giving your data a shared layer of meaning. Instead of teams each having their own defi

You’ve built a dazzling dashboard in Microsoft Fabric, and now external users are lining up like kids waiting for Santa. The question: how do you share the goodies without giving away the keys to the sleigh? Two Flavors of Holiday Cheer 1. Embed for Organization (UserOwnsData) Think of this as the office Secret Santa. Internal users bring their own badge (Microsoft Entra ID) and unwrap reports directly. Perfect for colleagues, not customers. 2. Embed for Customers (AppOwnsDat

If you’ve ever wondered whether to store your data in Microsoft Dataverse or OneLake , you’re not alone. It’s a common question, kind of like deciding whether to bring a spreadsheet or a data lake to a hackathon. Both are powerful, but they serve different purposes. What is Microsoft Dataverse? Dataverse is the backbone of the Power Platform and Dynamics 365 ecosystem. It’s a secure, cloud-based data service designed for business applications and low-code development. Think

As organizations embrace Microsoft Fabric for unified analytics, many teams are asking: How do we move from Azure Data Factory (ADF) to Fabric Data Factory (FDF)? This guide walks you through the migration process, highlights key differences, and shares best practices for a smooth transition. Why Fabric Data Factory? Fabric Data Factory brings the power of data integration and orchestration into the Fabric ecosystem. It offers: Power Query-based transformations with Dataflow

Modern cloud development demands speed, accuracy, and flexibility. GitHub Copilot powered by OpenAI brings AI-assisted coding directly into your workflow, helping you write SQL queries, optimize schemas, and integrate with Azure services faster than ever. Whether you are building data-driven apps or managing complex databases, Copilot can transform your development experience. Why GitHub Copilot for Azure and SQL? Copilot is not just for application code. It is a game-changer

If you are new to Microsoft Cloud and wondering how to make your data dance between Azure Data Factory and Power BI, you are in the right place. Think of Azure Data Factory as the choreographer and Power BI as the stage where your data performs. No complicated moves required. What You Need Before You Start An active Azure subscription A Power BI account Basic understanding of what data is (if you are still thinking “Excel but bigger,” you are on track) For official setup deta

Everyone loves Microsoft Fabric for unified analytics, but let’s face it, your data is still throwing a house party in Snowflake while its unstructured friends are lounging in S3, ADLS Gen2, and GCS like they own the place. The real puzzle is how do you invite all that chaos into OneLake without building a pipeline that looks like a bad hackathon project or duplicating data like a copy paste addict? This post gives you three smart ways to make it happen plus Microsoft Learn l