十月 30, 2024 作者 Patrick LeBlanc 48,020 视图
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 …
During the recent Ignite 2023 event, we announced the public preview of prebuilt AI services in Fabric. This integration with Azure AI services, formerly known as Azure Cognitive Services, allows for easy enhancement of data with prebuilt AI models without any prerequisites. Using AI services in Fabric has never been easier! In the past, you …
With the general availability of Microsoft Fabric this past Ignite, there are a lot of questions centered around the functionality of each component but more importantly, what architecture designs and solutions are best for analytics in Fabric. Specifically, how your data estate for analytics data warehousing/reporting will change or differ from existing designs and how to choose the right path moving forward. This article will be focused on helping you understand the differences between the Data Warehouse and Data Lakehouse, Fabric solution designs, warehouse/lakehouse use cases, and to get the best of both Data Warehouse and Data Lakehouse.
In today’s data-driven world, organizations face the challenge of efficiently managing and analyzing vast amounts of data. Microsoft OneLake provides a comprehensive solution that simplifies data lake management, enabling organizations to unlock valuable insights and maximize the potential of their data. To simplify interaction, OneLake seamlessly integrates with various tools and services covering a wide …
Continue reading “Working with OneLake using Azure Storage Explorer”
We are excited to announce automatic log checkpointing for Data Warehouses! One of our goals with the Data Warehouse is automate as much as possible to make it easier and cheaper for you to build and use them. This means you will be spending your time on adding and gaining insights from your data instead …
Continue reading “Announcing: Automatic Log Checkpointing for Fabric Warehouse”
We’re happy to announce a new function in Power Query’s M language for reading data from Delta Lake tables: the DeltaLake.Table function. This function is now available in Power Query in Power BI Desktop and in Dataflows Gen1 and Gen2 and replaces the need to use community-developed solutions like this one by Gerhard Brueckl. Let’s …
Continue reading “Read data from Delta Lake tables with the DeltaLake.Table M function”
