From Data to AI: Building Retail Intelligence with Fivetran + dbt Labs and Databricks
A live technical walkthrough of the full retail data architecture: from PostgreSQL ingestion into Databricks, through dbt Labs transformations and Unity Catalog governance, to AI-powered Genie applications.
September 29, 2026
12 PM EDT | 9 AM PDT
Virtual
Luis Leon
Ingestion: How Fivetran connects PostgreSQL data into Databricks, including what Lakebase changes about the architecture and when each pattern applies
Transformation: How dbt Labs converts raw Databricks data into analytics-ready tables with lineage, testing, and version control built in
Governance: How Unity Catalog manages access controls, lineage, and data trust across the full stack so AI applications are working with data that's verifiable, not just available
AI activation: Two Databricks Genie applications built on this architecture, with a close walkthrough of the process behind each and the retail workflows they support
You're all set, [Name].
Registration closed.
Retail AI depends on a data layer most demos skip. Before a Genie application can answer a business question, someone has to connect the data sources, run governed transformations, and make sure the data is trustworthy. That work falls to the data team.
This session is for the teams doing that work. We'll walk through the full architecture live: ingestion, transformation, governance, and AI activation so you leave with a concrete pattern you can bring back to your org.
Why now: Retailers are moving fast to deploy AI-driven applications. Most of those applications depend on a data foundation that takes months to get right. This session shows how to accelerate that foundation using Fivetran, dbt Labs, and Databricks together with a clear picture of where each tool fits.
Ingestion: How Fivetran connects PostgreSQL data into Databricks, including what Lakebase changes about the architecture and when each pattern applies
Transformation: How dbt Labs converts raw Databricks data into analytics-ready tables with lineage, testing, and version control built in
Governance: How Unity Catalog manages access controls, lineage, and data trust across the full stack so AI applications are working with data that's verifiable, not just available
AI activation: Two Databricks Genie applications built on this architecture, with a close walkthrough of the process behind each and the retail workflows they support
The full cycle: How ingestion decisions affect what transformations are possible, how transformation quality determines what AI can do, and how governance closes the loop from data to business action
Luis Leon
Luis Leon
Retail AI depends on a data layer most demos skip. Before a Genie application can answer a business question, someone has to connect the data sources, run governed transformations, and make sure the data is trustworthy. That work falls to the data team.
This session is for the teams doing that work. We'll walk through the full architecture live: ingestion, transformation, governance, and AI activation so you leave with a concrete pattern you can bring back to your org.
Why now: Retailers are moving fast to deploy AI-driven applications. Most of those applications depend on a data foundation that takes months to get right. This session shows how to accelerate that foundation using Fivetran, dbt Labs, and Databricks together with a clear picture of where each tool fits.