Automating your Power BI dataset refresh can save time and ensure your reports always stay up to date. In this post, we’ll walk through how to trigger a Power BI report refresh directly from Azure Data Factory (ADF) using an App Registration in Azure Entra ID.
There was a time when moving data from multiple sources felt like untangling a giant knot. Every data refresh meant scripts breaking, manual checks, and long hours spent ensuring everything flowed from source to destination correctly. Then Azure Data Factory (ADF) entered the picture, and it didn’t just simplify ETL and ELT. It completely transformed how we think about data orchestration at scale.
Azure AI Foundry is a powerful platform for developing and scaling AI solutions. It gives teams structure through hubs and projects, shared resources, and collaborative tools. But to get the most from Foundry, it is important to plan carefully. From resource organization to cost management, a little forethought can make your AI journey smoother and more efficient.
A project in Azure AI Foundry is a workspace designed for a specific AI development effort. Each project connects to a hub, giving it access to shared resources while also providing its own dedicated environment for collaboration and experimentation.
A hub is the foundation of Azure AI Foundry. Think of it as a control center where all the shared resources, security settings, and configurations for your AI development live. Without at least one hub, you cannot use the full power of Foundry’s solution development capabilities.
Azure AI Foundry is Microsoft’s dedicated platform for building, managing, and scaling AI solutions in the cloud. It is not just a collection of services, it is a structured environment designed to make AI development more efficient, organized, and secure.
Imagine you’re about to build something amazing with Azure AI. Before you dive into writing code or training models, there’s one big question: how do you set up your AI resources? This step might feel like just a checkbox, but it’s the foundation of how your application will scale, perform, and even stay within budget.
While working with Microsoft Fabric, a question came to mind: why use a Data Warehouse if the Lakehouse already provides a SQL endpoint? At first glance, it may seem redundant. However, when you look closer, the two serve very different purposes, and understanding these differences is key to knowing when to use each.
In the cloud data world, Microsoft Azure and Snowflake often come up as leading choices for building scalable data platforms. While they overlap in some capabilities, their core strengths and ecosystem focus make them suited to different use cases.
When organizations talk about becoming data-driven, the debate often comes down to where should data live and how should it be structured? That’s where the Data Lake and the Data Warehouse come into play. Both are critical, but their purposes and strengths differ.
In today’s cloud-first world, enterprises have no shortage of data services. But when it comes to building scalable, reliable data pipelines, two names often dominate the conversation: Azure Data Factory (ADF) and Azure Databricks.
When it comes to building a modern data platform in Azure, two technologies often spark debate: Microsoft Fabric and Databricks. Both are powerful. Both can process, transform, and analyze data. But they serve different purposes, and the smartest organizations know when to use each.
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