oktober 30, 2024 av Patrick LeBlanc 8 700 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 …
We are excited to share guidance for how you can use Microsoft Fabric to turn your data into knowledge for Generative AI applications. This guide will walk you through implementing a RAG (Retrieval Augmented Generation) system in Microsoft Fabric using Azure OpenAI and Azure AI Search. By the end, you’ll be more familiar with how to …
We are excited to announce the Capacity Pools for Data Engineering and Data Science in Microsoft Fabric. As part of the Data Engineering and Science settings in the Admin portal, capacity administrators can create custom pools based on their workload requirements. Optimizing Cloud Spend and Managing Compute Resources In enterprise environments, managing cloud spending and …
We’re excited to announce the introduction of new branching capabilities in Fabric Git integration, making it even easier to use! Fabric Git integration is a vital component of the Fabric CI/CD toolkit, enabling developers to connect their Fabric workspaces to Git repositories, and utilize Git features like version control and collaborative workflows. Before delving into …
Continue reading “Introducing New Branching Capabilities in Fabric Git Integration”
When we talk about Microsoft Fabric workspace collaboration, a common scenario is developers and their teams using a shared workspace environment, which means they have access to “live items”. A change made directly within a workspace would override and affect all other developers or users utilizing that workspace. This is where git becomes increasingly important …
Developing ETLs/ELTs can be a complex process when you add in business logic, large amounts of data, and the high volume of table data that needs to be moved from source to target. This is especially true in analytical workloads involving relational data when there is a need to either fully reload a table or incrementally update a table. Traditionally this is easily completed in a flavor of SQL (or name your favorite relational database). But a question is, how can we execute a mature, dynamic, and scalable ETL/ELT utilizing T-SQL with Microsoft Fabric? The answer is with Fabric Pipelines and Data Warehouse.
