Microsoft Fabric Updates Blog

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Microsoft Fabric December 2023 Update

Welcome to the December 2023 update.

We have lots of features this month including More styling options for column and bar charts, calculating distinct counts in Power BI running reports on KQL Databases, Changes to workspace retention settings in Fabric and Power BI, and many more.

Prebuilt Azure AI services in Fabric

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 …

Lakehouse vs Data Warehouse vs Real-Time Analytics/KQL Database: Deep Dive into Use Cases, Differences, and Architecture Designs

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.

Working with OneLake using Azure Storage Explorer

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 …

Announcing: Automatic Log Checkpointing for Fabric Warehouse

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 …