Schema lakehouses are now Generally Available. By using schemas in lakehouses, users can arrange their tables more efficiently and make it easier to find data. When creating new lakehouses, schema-enabled lakehouses will now be the default choice. However, users still have the option to create lakehouses without a schema if they prefer. What do schema … Continue reading “Lakehouse Schemas (Generally Available)”
Discover how Microsoft Fabric’s Forecasting Service system reduces Spark startup latency and cloud costs through proactive AI and ML-driven resource provisioning. Context & Relevance Waiting minutes for a Spark cluster to become available can throttle analytics velocity, delay insights, and drive-up cloud spend. In a world where data teams expect near‐instant execution and seamless burst … Continue reading “How does Fabric make Spark Notebooks Instant?”
In today’s data-driven world, the ability to quickly expose data through modern APIs is crucial. Microsoft Fabric’s API for GraphQL combined with Materialized Lake Views offers a powerful solution that bridges the gap between your Fabric LakeHouse data and application developers who need fast, flexible access to your data. In this guide, we’ll walk you … Continue reading “Exposing Lakehouse Materialized Views to applications in minutes with GraphQL APIs in Microsoft Fabric”
JDBC (Java Database Connectivity) is a widely adopted standard that enables client applications to connect to and work with data from databases and big data platforms. The Microsoft JDBC Driver for Microsoft Fabric Data Engineering (Preview) – an enterprise-grade connector that brings powerful, secure, and flexible Spark SQL connectivity to your Java applications and BI … Continue reading “Microsoft JDBC Driver for Microsoft Fabric Data Engineering (Preview)”
Data Engineers working with Microsoft Fabric often need to manage environment-specific configurations, including the modification of Lakehouse names, file paths, or schema names for development, testing, and production environments. In this scenario, you would want to avoid hard coding this information. This is where variable libraries in Fabric can help data engineers manage their environment configuration when working with Fabric User data … Continue reading “Manage environment configuration in Fabric User data functions with variable libraries “
