AI on Your Lakehouse: Context Comes in Shapes, Not Queries

Course Description

Your agent can reach your data but still can’t use it reliably. Vector search and Text2SQL each hand it a slice - similar passages, or a guessed SQL join - not the view to know what is truly relevant and how it connects. Answers come back confident but wrong. The problem isn’t caused by a bad model or bad query, but rather a lack of context, and context comes in shapes.

In this hands-on workshop, you give an agent three reusable shapes:

These shapes answer the questions a modern stack gets quietly wrong - not just the single lookups it already handles most of the time, but the estate-level ones: the patterns across an entire document set, what your records show happening that the documentation never covers, and how things connect across a boundary no one query spans. Each needs the agent to cover, navigate, or follow the whole structure - not retrieve a similar slice.

You work the pattern on AutoFix Group, a fictional auto-repair chain: manuals, bulletins, and recalls as PDFs alongside vehicles, work orders, and parts in a warehouse. In a hosted Codespace, you and your coding agent build a service-advisor skill shape by shape, then put it to work across both halves - federating the warehouse with the graph instead of migrating it.

The pattern is portable by design: the warehouse here is BigQuery, but swap the connector and the same shapes work on Snowflake, Databricks, or anywhere your data lives.

Prerequisites

Before taking this workshop, you should have:

For the hands-on path you will need a coding agent (this workshop assumes Claude Code) together with the Codespace - which provides read-only access to the BigQuery dataset and your Neo4j sandbox. The embedded sandbox window lets you inspect and query the graph by hand as you go.

Duration

2 hours core path, plus around 20 minutes of optional practice.

What you will learn