dlt & BigQuery Graph: From Raw Tables to Conversational Analytics in One Afternoon

A tutorial demonstrates a pipeline that ingests the relational Chinook sample database into BigQuery with dlt, creates canonical BigQuery views for core entities and relationships, declares a native property graph using BigQuery’s CREATE PROPERTY GRAPH feature, and queries it through GQL and Gemini-powered Conversational Analytics. The graph uses eight node types and eight edge types without moving data, enabling relationship-focused queries such as tracing a customer’s purchases through invoices, tracks, albums, and artists with a readable multi-hop MATCH pattern rather than manually constructed joins or recursive SQL. In testing, Conversational Analytics correctly translated a plain-English question about a customer’s purchased artists into GQL, returned 15 artists, and suggested additional insights and follow-up questions. The post argues that the central requirement for reliable graph and natural-language results is a well-defined canonical data model and ontology created upstream, because graph structure and AI interfaces cannot correct ambiguous or incorrect business meaning. It also notes implementation requirements including explicit keys for every graph node and edge table, fully qualified table references, and avoiding reserved keywords such as CONTAINS for edge labels.

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