9 min. read
Google is bundling BigQuery, AlloyDB, Spanner, and Apache Spark under one name, calling the result Agentic Data Cloud. That sounds like marketing. But it becomes concrete when you take a closer look at the cross-cloud lakehouse announcement: BigQuery can now query Iceberg tables on Amazon S3 without egress fees, via Google’s own Cross-Cloud Interconnect lines. This isn’t a feature announcement. This is a pricing argument against Databricks
Cross-Cloud Lakehouse: the real offensive
The part that becomes relevant in Databricks customer conversations is not the agentic branding. It is the Cross-Cloud Lakehouse. BigQuery can now read and write Iceberg tables that are physically located on Amazon S3 or Azure Data Lake Storage. The connection runs via Cross-Cloud Interconnect – a dedicated private line, no public internet, no egress fees.
This is significant because it overturns the data egress argument. Previously, a BigQuery migration was often expensive because data had to be moved out of S3 or Azure. Those who currently operate Databricks on AWS and want to test BigQuery at least in parallel no longer need to plan for large data movements. The Lakehouse resides on S3, BigQuery accesses it directly.
Key Figures
- American Express is migrating a central on-premises data warehouse as well as several hundred production applications to BigQuery for agent-based commerce platforms
- Google states around 9 months as the average migration duration for cloud-to-cloud transitions – previously, multi-year projects were the standard
- 6 Catalog Partners federated: AWS Glue, Databricks Unity Catalog, Snowflake Polaris, SAP, Salesforce, and Confluent Tableflow (coming soon)
- Managed Iceberg GA since April 2026: Multi-Table Transactions, CDC, and History-Based Optimizations in BigQuery Lakehouse
Catalog Federation and the Databricks Bone of Contention
Catalog Federation is the second major lever. Google has announced bidirectional federation for Databricks Unity Catalog, Snowflake Polaris, and AWS Glue. Engines can directly write to and read from each other’s catalog – without data copies, without ETL pipelines in between. Zero-Copy-Sharing.
For DACH companies that already operate Databricks and are wondering if BigQuery is a better fit for certain workloads, this is a different conversation than it was twelve months ago. You no longer have to decide. You can start to selectively shift workloads while the data foundation remains on S3 or in Databricks.
“Migration is no longer a multi-year project. Cloud-to-cloud moves now take nine months on average. The question is not whether, but which workload first.”
BigQuery Lakehouse vs. Databricks for DACH Architectures
In DACH enterprise environments, the discussion usually revolves around three axes: governance and data sovereignty, Total Cost of Ownership, and connectivity to existing SAP landscapes. Google has directly addressed all three.
BigQuery Lakehouse
- Serverless, no cluster management
- Built-in Gemini integration without extra connectors
- Managed Iceberg with multi-table transactions
- SAP Cortex connectors out of the box
- Cross-cloud without egress costs (preview)
Databricks
- Deeper Spark ML workflows for data scientists
- Unity Catalog as a mature open standard
- MLflow, Delta Lake, and Mosaic AI deeply integrated
- Stronger in multi-cloud without GCP dependency
- Broader ecosystem independence
Those primarily building analytics, BI, and agent context within the GCP ecosystem now have a serious argument for BigQuery Lakehouse. Those operating Spark-based Machine Learning workflows with data science teams and wishing to remain cloud-agnostic still hold the better cards with Databricks.
What this means for the 2026 decision
The context has changed. Anyone who said in 2024, “we’ll stick with Databricks because switching to BigQuery costs too much” – the egress argument is limited once Cross-Cloud Lakehouse becomes available in their own region. Data movement is no longer a primary cost factor.
What remains are the real questions: Which workloads are GCP-centric? Where do we operate Machine Learning beyond analytics? How deep is our SAP integration? These answers decide – no longer the egress tariff.
For DACH architects, this means: The next data platform tender is worth recalibrating. Not because BigQuery is automatically better now – but because Google Cloud Next 2026 has shifted the basis for comparison. Databricks will react to this. The rest of the year will show how.
Frequently Asked Questions
What is Google Agentic Data Cloud and how does it differ from previous BigQuery offerings?
Agentic Data Cloud is Google’s strategic umbrella encompassing AlloyDB, BigQuery, Spanner, Bigtable, and Managed Apache Spark. The difference from previous BigQuery offerings lies in its focus: shifting from human analysis to machine data consumption by AI agents. The Universal Context Engine, as a new layer, aims to prevent hallucinations by providing agents with direct, structured
