oktober 30, 2024 av Patrick LeBlanc 8 405 Visninger
Welcome to the October 2024 Update! Here are a few, select highlights of the many we have for Fabric this month. API for GraphQL support for Service Principal Names (SPNs). Introducing a powerful new feature in Lakehouses: Sorting, Filtering, and Searching capabilities. An addition to KQL Queryset that will revolutionize the way you interact with …
Shortcuts in Microsoft OneLake allow you to unify your data across domains and clouds by creating a single virtual data lake for your entire enterprise. With shortcuts, data can be reused multiple times, making it simple to consolidate data, without data movement, data duplication or changing ownership of the data. The consumption of data via …
Introduction: We are excited to announce a significant enhancement to the Tenant Setting Delegation feature in Microsoft Fabric. Tenant setting delegation has existed in some form since the public preview launch of Fabric, primarily allowing delegation of workload controls to capacity admins. We are now introducing the delegation of export settings to workspaces via domain. …
The Share capability for Fabric AI Skill is now in public preview. This feature enables AI Skill creators to share their AI Skill with colleagues, allowing them to consume the AI Skill and gain valuable insights from the data. How Sharing WorksOnce you have created and validated the effectiveness of your AI Skill, you can …
Continue reading “Announcing Public Preview of the “Share” Feature for Fabric AI Skill”
In the era of Generative Artificial Intelligence, data warehouses play a pivotal role for organizations to store, manage, and analyze massive amounts of data. They serve as the foundation for business intelligence and help make informed decisions. However, like any technology, data warehouses are not immune to failures – whether from hardware malfunctions, software bugs, …
Continue reading “Ensuring Data Continuity in Fabric Warehouse: Best Practices for Every Scenario”
Guidance to Maximizing Productivity and Efficiency for your Data Engineering Workloads Data engineering teams often grapple with the complexities of planning and configuring compute resources for their data platforms. This is especially true when working with large-scale, complex datasets and demanding downstream SLAs. A one-size-fits-all approach is rarely effective, as different data layers and datasets …
Continue reading “Optimizing Spark Compute for Medallion Architectures in Microsoft Fabric”
