Virtual Hands-On Lab: Zero to Genie
Building an AI-Ready Manufacturing Lakehouse in Minutes
July 2, 2026
Great Britain UK (BST): 10:00 AM - 11:30 AM Central Europe (CEST): 11:00 AM - 12:30 PM
Virtual
Aymen Ben Azouz
Automate ingestion to Unity Catalog: Use Fivetran to extract and load ERP, MES, and sensor data into a governed Databricks Unity Catalog environment.
Architect with dbt: Deploy a pre-built dbt project to transform raw data into a standardised data model for production, quality, and supply chain.
Deploy a GenAI agent: Configure a Databricks Genie Space to act as an intelligent AI, querying performance and inventory risk using natural language.
Master Open Data format: Orchestrate an end-to-end lifecycle that removes brittle integrations and supports advanced AI workloads.
You're all set, [Name].
Registration closed.
Manufacturing data powers modern factories, but it’s still stuck in siloed ERP, MES, and fragmented sensor systems. The result? Engineering teams spend more time maintaining fragile pipelines than delivering insights that improve yield and reduce downtime. What’s needed is an AI-first foundation — one that replaces manual integration with automated, governed data flows.
In this hands-on lab, you’ll build that foundation using Fivetran and the Databricks Data Intelligence Platform. You’ll centralise shop-floor and enterprise data into a unified Lakehouse, then activate it with Databricks Genie. By the end, you’ll have a conversational AI interface that answers complex operational questions — turning raw data into real-time insights for demand forecasting, supplier variability, and inventory risk, without pipeline overhead.
Automate ingestion to Unity Catalog: Use Fivetran to extract and load ERP, MES, and sensor data into a governed Databricks Unity Catalog environment.
Architect with dbt: Deploy a pre-built dbt project to transform raw data into a standardised data model for production, quality, and supply chain.
Deploy a GenAI agent: Configure a Databricks Genie Space to act as an intelligent AI, querying performance and inventory risk using natural language.
Master Open Data format: Orchestrate an end-to-end lifecycle that removes brittle integrations and supports advanced AI workloads.
Aymen Ben Azouz
Aymen Ben Azouz
Manufacturing data powers modern factories, but it’s still stuck in siloed ERP, MES, and fragmented sensor systems. The result? Engineering teams spend more time maintaining fragile pipelines than delivering insights that improve yield and reduce downtime. What’s needed is an AI-first foundation — one that replaces manual integration with automated, governed data flows.
In this hands-on lab, you’ll build that foundation using Fivetran and the Databricks Data Intelligence Platform. You’ll centralise shop-floor and enterprise data into a unified Lakehouse, then activate it with Databricks Genie. By the end, you’ll have a conversational AI interface that answers complex operational questions — turning raw data into real-time insights for demand forecasting, supplier variability, and inventory risk, without pipeline overhead.