Query Your AWS Data Directly From Google Cloud

Google BigQuery now lets you analyze data on AWS and Azure without moving it, simplifying multi-cloud operations and cutting transfer costs.

Google is breaking down the walls between cloud data silos with a major update to its BigQuery analytics platform. According to a recent announcement on the Google Cloud Blog, BigQuery can now directly query data stored in other major clouds, including Amazon Web Services (AWS) and Microsoft Azure. This new capability allows organizations to analyze their information where it lives, eliminating the need for costly and complex data-moving operations. The key to this feature is its support for Apache Iceberg, an open-source data format that is rapidly becoming an industry standard. Instead of forcing customers to migrate all their data into Google’s ecosystem to analyze it, Google is now meeting them where their data already resides, signaling a significant strategic shift toward interoperability in the cloud data wars.

The new functionality works by treating data in external cloud storage as if it were native to BigQuery. The technical enabler is Apache Iceberg, an open table format designed for enormous analytic datasets. Iceberg essentially creates a metadata layer on top of files in object storage like Amazon S3 or Azure Data Lake Storage (ADLS) Gen2. This layer organizes the raw data files into a structured, queryable table. Google BigQuery’s engine can now read these Iceberg tables directly, regardless of their physical location. When a user runs a query, BigQuery sends the computation instructions to the data's location, processes it in place, and returns only the final results. This approach, known as data federation, avoids the slow and expensive process of extracting, transforming, and loading (ETL) data from one cloud to another just for analysis.

This move is part of a broader industry trend away from monolithic, locked-in data platforms and toward a more flexible, disaggregated model. For years, the primary business model for cloud data warehouses was to centralize all of an organization's data. However, as companies increasingly adopt multi-cloud strategies for cost optimization, feature specialization, or M&A activity, their data has become naturally fragmented. Competitors like Snowflake and Databricks have also heavily invested in supporting open standards like Iceberg to address this reality. By embracing an open, cross-cloud approach, Google is acknowledging that vendor lock-in is a major concern for customers. It’s a strategic play to keep BigQuery relevant and competitive, positioning it not just as a data destination but as a powerful, universal query engine for a company's entire data estate.

For CTOs, data architects, and engineering teams, this development has immediate practical implications. It provides a path to simplify complex data architectures and significantly reduce cloud infrastructure costs, particularly the notorious egress fees charged for moving data out of a cloud provider's network. Teams can now build more agile analytics and AI systems that draw on data from multiple sources without being forced into a massive migration project. Looking ahead, the focus will likely shift toward optimizing the performance, security, and governance of these cross-cloud queries. As this model matures, we can expect to see deeper integrations and more sophisticated tools for managing a unified data ecosystem that spans multiple providers. The key question now is how this increased interoperability will reshape the competitive landscape and whether it will truly deliver on the promise of a seamless multi-cloud experience.

For data engineers and architects, this update eliminates the need for brittle, expensive data pipelines between clouds. It allows teams to query data in-place using open standards, reducing vendor lock-in and simplifying the architecture required for comprehensive, multi-cloud analytics and AI models.

This move directly reduces cloud spending by eliminating hefty data egress fees for moving petabytes between providers. Businesses can now get faster insights from all their data, regardless of where it lives, improving decision-making without the cost and security risks of duplicating sensitive information.