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Eddie Edgeworth

Speaker

Eddie Edgeworth

2

talks

CTO Koantek

Eddie Edgeworth is Chief Technology Officer at Koantek, a four-time Databricks Partner of the Year, where he leads award-winning IP accelerators such as X2D and Ascend AI. A Databricks MVP, Developer Champion, and member of the Databricks Product Advisory Board, he has been building on the platform since 2016. With 25 years in data architecture, cloud engineering, and AI-driven productization, he has delivered lakehouse and governance solutions that slash TCO by 60 percent and accelerate value for Fortune 500 enterprises across healthcare, finance, and manufacturing. Eddie’s passion is turning complex data challenges into scalable, compliant outcomes.

Bio from: Data + AI Summit 2025

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No-Trust, All Value: Monetizing Analytics With Databricks Clean Rooms

In a world where data collaboration is essential but trust is scarce, Databricks Clean Rooms delivers a game-changing model: no data shared, all value gained. Discover how data providers can unlock new revenue streams by launching subscription-based analytics and “Built-on-Databricks” services that run on customer data — without exposing raw data or violating compliance. Clean Rooms integrates Unity Catalog’s governance, Delta Sharing’s secure exchange and serverless compute to enable true multi-party collaboration — without moving data. See how privacy-preserving models like fraud detection, clinical analytics and ad measurement become scalable, productizable and monetizable across industries. Walk away with a proven pattern to productize analytics, preserve compliance and turn trustless collaboration into recurring revenue.

Sponsored by: Domo | Orchestrating Fleet Intelligence with AI Agents and Real-Time IoT With Databricks + DOMO

In today’s logistics landscape, operational continuity depends on real time awareness and proactive decision making. This session presents an AI agent driven solution built on Databricks that transforms real time fleet IoT data into autonomous workflows. Streaming telemetry such as bearing vibration data is ingested and analyzed using FFT to detect anomalies. When a critical pattern is found, an AI agent diagnoses root causes and simulates asset behavior as a digital twin, factoring in geolocation, routing, and context. The agent then generates a corrective strategy by identifying service sites, skilled personnel, and parts, estimating repair time, and orchestrating reroutes. It evaluates alternate delivery vehicles and creates transfer plans for critical shipments. The system features human AI collaboration, enabling teams to review and execute plans. Learn how this architecture reduces downtime and drives resilient, adaptive fleet management.