"Microsoft is adding two new features to its cloud-based Fabric to help agentic applications make more accurate decisions while navigating complex workflows and help developers integrate AI-driven insights directly into an enterprise's data operations. The two new features - Graph and Maps - are being integrated into Fabric's Real-Time Intelligence workload. Fabric, released in May 2023, brings together six "workloads": Data Factory, Data Engineering, Data Warehouse, Data Science, Real-Time Intelligence, and Power BI,"
"The Real-Time Intelligence workload, specifically, was built to help enterprises make better decisions by generating insights from real-time data. It combines mainly two modules - Synapse Real-Time Analytics and Data Activator, along with other functions. The Graph feature has been taken from LinkedIn's graph database technology that supports visual exploration as well as natural language querying, according to Arun Ulagaratchagan, corporate vice president of Azure Data at Microsoft."
"Explaining the rationale behind the usage of LinkedIn's graph technology, Ulagaratchagan said that Microsoft itself did not have a mature, full-scale graph database to build on. That gap, he added, was bridged when a LinkedIn engineering team specializing in graph technology was moved under his leadership "a while back", giving Fabric a direct line to the social network's "proven expertise" in modelling complex relationships at scale."
Microsoft added Graph and Maps to Fabric's Real-Time Intelligence workload and introduced an MCP Server to enable developers to connect agents to Fabric's ecosystem to accelerate tasks such as creating a data pipeline or notebook. Fabric, released in May 2023, unifies six workloads: Data Factory, Data Engineering, Data Warehouse, Data Science, Real-Time Intelligence, and Power BI. Real-Time Intelligence generates insights from real-time data and combines Synapse Real-Time Analytics with Data Activator. Graph leverages LinkedIn's graph database technology to support visual exploration and natural-language querying. A LinkedIn engineering team was moved under Azure Data leadership, providing proven expertise in modeling relationships at scale. An analyst said reusing LinkedIn's graph foundation reduces risk and speeds adoption.
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