BigQuery Graph is now GA: the knowledge foundation for the agentic era

The useful part

Many of the questions that matter in enterprise data aren't just about individual rows — they're about how things connect: how two accounts are linked, what path a payment took, what context grounds an AI agent's answer. Historically, unlocking these insights meant extracting data into standalone graph databases, creating silos and operational overhead. To remove these barriers, we brought native graph capabilities directly to the data warehouse.

How it works

• It reaches petabyte-scale without the memory bottlenecks of a scale-up database, runs under your existing row- and column-level security, and calls BigQuery ML and AI functions in the same query.
• By modeling our security signal data as a property graph, we can now perform complex, multi-hop traversals in seconds.
• We introduced BigQuery Graph in preview to unify graph and relational analytics.
• ISO-standard Graph Query Language (GQL) sits alongside SQL, traversals run natively, and there's no ETL.
• Because it's built on BigQuery, BigQuery Graph inherits and expands its capabilities:

What to take from it

One engine, two jobs — large-scale graph analytics, and connected context for AI agents. "BigQuery Graph has been a game-changer for our threat detection pipeline, allowing us to move beyond simple, siloed alerts. This graph-centric approach automatically clusters anomalies into coherent attack stories, which, combined with the seamless integration of Gemini models, helps us generate actionable threat narratives.

Details worth keeping

Today, we are announcing the general availability of BigQuery Graph. something that was previously computationally prohibitive. We look forward to integrating native BigQuery Graph al...