Siemens, Databricks and FFT integrate Industrial Edge with Databricks Platform
- Siemens Industrial Edge streams contextualized shopfloor and plant data via FFT DataBridge directly to the Databricks Platform without IoT middleware.
- Centralized AI models are trained on Databricks and can be deployed back to the edge for low-latency execution and closed-loop workflows.
- Targeted use cases include predictive maintenance, quality optimisation, energy management, supply chain optimisation and agentic AI.
- FFT says DataBridge is ready to use for more than 30,000 potential customers and removes the need for expensive, time‑intensive data transformation.
What the integration does
The partners connect shopfloor and plant data from an edge platform directly to a cloud AI platform, eliminating the need for separate IoT middleware. Contextualized production data is streamed from Industrial Edge via the FFT DataBridge into the Databricks Platform for analysis and model training.
Data flow and architecture
Data is captured and contextualized at the edge, streamed to a governed, cloud‑agnostic analytics layer for model training, then deployed back to the edge for execution. The setup supports low latency, high availability and secure operation, enabling closed‑loop and physical AI workflows.
Use cases and benefits
The combined stack is positioned to support predictive maintenance, quality optimisation, energy management, supply chain optimisation and agentic AI, helping customers optimise operations, reduce costs and increase productivity.
Partner roles
Siemens provides the Industrial Edge and integration layer; Databricks supplies cloud analytics, machine learning and agentic AI capabilities; FFT supplies the DataBridge to securely stream contextualized, AI‑ready production data and bridge IT and OT for customers.
Source: Siemens
