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. That’s what a graph is built to solve. 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. Today, we are announcing the general availability of BigQuery Graph.
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. And because it’s built on BigQuery, BigQuery Graph inherits and expands its capabilities: 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. 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. By modeling our security signal data as a property graph, we can now perform complex, multi-hop traversals in seconds – something that was previously computationally prohibitive. 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. We look forward to integrating native BigQuery Graph algorithms to further streamline our workflows.” – Pete Rubio, VP of Global engineering at Thales Cybersecurity Products
Since preview, we saw data teams across industries adopt BigQuery Graph for both analytical and agentic workflows:
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Threat and fraud detection: Security and financial organizations correlate signals across event logs to uncover multi-hop attack paths, fraud networks, and suspicious transaction loops.
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Supply chain digital twins: Manufacturing and logistics organizations map dependencies across suppliers, parts, and distribution routes to simulate disruptions and optimize fulfillment.
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Identity resolution and Customer 360: Ad-tech and retail platforms stitch fragmented user identifiers and behavioral touchpoints into unified customer profiles across channels.
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Knowledge graphs and AI agent grounding: Enterprise AI teams build structured knowledge graphs from unstructured documents, providing domain context to ground Gemini models and GraphRAG workflows.
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Network lineage and infrastructure management: Telecommunications and enterprise IT teams track complex network topologies, service dependencies, and data lineage across multi-hop paths.
What’s new in BigQuery Graph
Reaching GA is more than a stability milestone. The work fell into two movements: we made the graph engine itself faster and broader, and we built an agentic ecosystem around it — so agents can build a graph, chat with it, and keep an auditable memory on it. Some of what follows is generally available today; some is in preview or rolling out over the coming weeks.
A faster, broader graph engine
“Advertising has spent decades optimizing individual events; the agentic era will optimize the relationships between them. At Yahoo, BigQuery Graph gives our AI agents connected context – campaigns, audiences, exposures, and outcomes, traversable with standard GQL right where our monetization data already lives, with no separate graph engine and no data movement. Our agents don’t just read the graph; they reason over it and write their conclusions back as new relationships. That’s how monetization moves beyond automation, to autonomous systems we can trust to act.” – Mikul Bhatt, Director of Engineering, Monetization Platform at Yahoo
Borderless graph Lakehouse
Agents are only as good as the context they can reason over, and that context is rarely in one place. With borderless Lakehouse, a single BigQuery Graph can span native BigQuery tables and open Iceberg tables in other clouds — through Databricks Unity Catalog, AWS Glue, or Snowflake — traversed in place, without copying data or building ETL pipelines.
Say a support agent needs to answer, “who supplies the product behind this customer’s delayed order, and where are they based?” The customer data sits in an Iceberg lakehouse on Google Cloud, the product and supplier records in a Databricks catalog on AWS. Instead of stitching the sources together per request, the agent traverses one virtual knowledge graph that already connects them — over data that never moved.
