Microsoft Fabric Updates Blog

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How Spark Supports OneLake Security with Row and Column Level Policies

Recently, we announced a significant milestone: public support for Row and Column Level Security within OneLake. This universal security framework applies consistently across all data engines, regardless of how data is accessed. Traditionally, Spark does not provide granular security features and assumes unrestricted access to the required datasets for query execution. To address this limitation, …

Simplifying Data Ingestion with Copy job – Copy data across tenants using Copy job in Fabric Data Factory 

Copy job is the go-to solution in Microsoft Fabric Data Factory for simplified data movement, whether you’re moving data across clouds, from on-premises systems, or between services. With native support for multiple delivery styles, including bulk copy, incremental copy, and change data capture (CDC) replication, Copy job offers the flexibility to handle a wide range …

Capacity usage enabled date for Test capability in User Data Functions

User Data Functions is an item that allows you to create fully managed Python functions to run on Fabric. You can use this item to embed your business logic in your Fabric data architectures across all the different integrations, such as Fabric data sources, Power BI reports, Pipelines, Notebooks, and more. The use of the …

Unlock Real-Time Intelligence with the Eventhouse Endpoint for Lakehouse

The Eventhouse Endpoint for Lakehouse is a powerful new capability in Microsoft Fabric that enables users to query Lakehouse tables with exceptional speed and ease, delivering real-time insights with high performance with large data volume, flexibility, advanced analytics capabilities, support for enhanced data formats such as strings and dynamic types and simplicity. Whether you’re working …

External data materialization in Fabric Data Warehouse

Data warehouses are at the core of modern analytics, enabling organizations to turn raw data into actionable insights. Fabric Data Warehouse empowers data professionals to analyze various types of data from the classic DW table to the Parquet, CSV, and JSONL files. One of the most important decisions when working with external data is how …