AI-Ready Industrial Data from Edge to Lakehouse

Connect plant-floor systems and prepare operational data for Databricks analytics, AI, and manufacturing intelligence.

• Connect PLCs, SCADA, historians, sensors, CNCs, MES, and production systems
• Use 250+ native drivers to reduce custom integration
• Consistent data foundation for Databricks Lakehouse and AI

Turn raw machine signals into structured operational data before sending it to Databricks.

• Normalize tags, units, timestamps, and data types
• Add asset, line, machine, work center, and process context
• Prepare high-fidelity data for Delta Lake, Unity Catalog, and AI workflows

Publish industrial data directly fromo Litmus Edge into Databricks Unity Catalog Delta Tables using Zerobus ingest.

• Publish industrial data directly into Delta Tables
• Support JSON payloads from DeviceHub and analytics
• Use governed ingestion with service principals, catalogs, and schemas

Move industrial data into Databricks with a scalable edge-to-Lakehouse integration pattern.

• Deliver real-time and historical OT data into Databricks
• Reduce reliance on middleware, brokers, and duplicate storage layers
• Scale pipelines across sites and global operations

Support both real-time manufacturing use cases and deeper historical analysis in Databricks.

• Stream high-frequency OT data for near real-time insights
• Send historical datasets for model training, trend analysis, and benchmarking
• Organize data using Bronze, Silver, and Gold medallion layers

Run processing close to machines and support feedback loops between Databricks and the plant floor.

• Filter, enrich, and transform data at the edge
• Run KPIs, alerts, thresholds, and event logic locally
• Train and improve models in Databricks and deploy decisions back to the edge

Control how industrial data moves into Databricks, making trusted data accessible for enterprise analytics and AI.

• Role-based access control
• Encryption in transit and at rest
• Govern how data flows into Databricks and is consumed