If you have SAP running your business and Google Cloud running your analytics, you have spent years managing the gap between them. ETL pipelines, data copies, replication lags, and the never-ending question of which source is actually correct. SAP Business Data Cloud (BDC) Connect for Google BigQuery is now generally available and addresses that problem directly.

The feature went GA on July 27, 2026, and it is a significant milestone for enterprise data teams that have been waiting for a clean path between SAP’s operational data and the analytics and AI workloads sitting in BigQuery.

What It Does

BDC Connect provides zero-copy, bi-directional access between SAP Business Data Cloud and Google BigQuery. That means your analysts and AI agents can query live SAP data directly inside BigQuery without moving it, duplicating it, or waiting for a pipeline to run.

The integration preserves SAP’s business semantics, so when you pull a field from your ERP, it carries the same meaning and context it has inside SAP. That matters more than it sounds. One of the biggest failure modes in enterprise analytics is data that arrives in an analytics environment stripped of its business logic, forcing teams to rebuild meaning that already existed in the source system.

Key capabilities include:

• Zero-copy queries: Analysts query SAP data from BigQuery without paying data transfer or storage costs for duplicated records
• Bi-directional enrichment: Enriched data, including external datasets like Google Trends or geospatial data from Google Maps, can be published back to SAP
• Multi-agent workflows: Agents built on the Gemini Enterprise Agent Platform or SAP Joule can now orchestrate across both systems using live data
• Natural language analytics: Analysts can ask questions in plain English and get context-aware answers grounded in real-time SAP records

Why This Matters Now

The timing is not coincidental. Enterprise AI deployments are running into a consistent problem: agents that cannot access live operational data are limited to working with yesterday’s information. When an AI agent is trying to re-route a supplier order or optimize inventory in real time, stale data is not a minor inconvenience. It can make the agent’s output worse than a manual decision.

This integration changes the calculus for AI deployments built on top of SAP data. Deloitte has already demonstrated this in autonomous procurement, building an agent that simultaneously accesses live SAP inventory data and external market pricing. That agent reportedly saved significant cost by re-routing orders autonomously when supplier conditions shifted, without waiting for a human to pull a report and make a call.

ElringKlinger, an automotive supplier, is using the integration to run real-time inventory reporting without creating data copies of their ERP. They can now query production data live and build AI-assisted analytics on top of it without maintaining a separate data warehouse for SAP records.

What This Means for Business

For organisations running SAP as their ERP backbone, this removes one of the most persistent arguments against building serious analytics and AI on top of operational data: the cost and complexity of getting that data into an analytics-friendly environment.

The zero-copy architecture has two practical effects. First, it eliminates the infrastructure overhead of maintaining sync processes. Second, it removes the governance headache of keeping two copies of sensitive business data in sync. Analysts work from one authoritative source. There is no version of the data sitting in BigQuery that is slightly behind or subtly different.

For AI agents, the implications go further. Agents that need to make decisions, not just produce reports, require access to data that is current enough to act on. Procurement agents, supply chain agents, and finance automation workflows all benefit from querying the actual state of the business rather than a snapshot from the last pipeline run.

The integration is available globally for SAP Business Data Cloud deployments on AWS and Google Cloud. Azure support is listed as coming soon.

What Enterprise DNA Sees Here

This is what the maturation of enterprise AI infrastructure looks like. Not a headline model release, but a foundational capability: reducing the friction between where business data lives and where AI can act on it.

For data teams that have been building out Power BI reports, Python pipelines, and SQL analytics on SAP exports, this integration changes the architecture conversation. The question is no longer “how do we get SAP data somewhere useful?” It is “what can we build now that the data is already there?”

For businesses exploring AI agents through Omni Ops or similar solutions, the ability to ground those agents in live ERP data rather than replicated copies is the difference between a demo and a production deployment.

Data teams that have held off on agentic AI because they could not guarantee data freshness now have fewer reasons to wait.