Google Cloud Introduces Cross-Engine Iceberg Support in BigQuery

Google Cloud Introduces Cross-Engine Iceberg Support in BigQuery - InfoQ

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[InfoQ Homepage](https://www.infoq.com/ "InfoQ Homepage")[News](https://www.infoq.com/news "News")Google Cloud Introduces Cross-Engine Iceberg Support in BigQuery

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Google Cloud Introduces Cross-Engine Iceberg Support in BigQuery

May 23, 2026 3 min read

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At the Apache Iceberg Summit last month, Google announced new interoperability features for Apache Iceberg in BigQuery. The preview of the serverless Iceberg REST catalog lets teams create, update, and query the same Apache Iceberg tables in BigQuery and in engines like Spark, Flink, and Trino without duplicating data.

The preview allows multiple tools to work on the same datasets without copying data or relying on proprietary formats. The cloud provider also introduced managed support for metadata, table maintenance, and synchronization tasks commonly handled manually in Iceberg deployments. Yuriy Zhovtobryukh, senior product manager at Google, and Angela Soares, senior product marketing manager at Google, explain why it matters:

If you're building a lakehouse today, you're probably using Apache Iceberg, which has gained massive popularity among data platform teams that need to support multiple compute engines (like Spark and BigQuery) that access the same data for different workloads.

At the recent Next ’26, Google has expanded Iceberg interoperability into a cross-cloud lakehouse, supporting querying of Iceberg catalogs across AWS, Azure, Databricks, and Snowflake, as well as AI workflows. According to Google, the overall goal is to enable organizations to keep data in open formats while using different processing and analytics tools on the same datasets.

Google argues that many teams using Apache Iceberg still face higher costs and operational complexity compared with fully managed data platforms, especially for streaming data, replication pipelines, and governance across multiple tools. To address this, Google is extending its BigQuery infrastructure to support Iceberg tables, including managed metadata, automatic table maintenance, transactions, and change data replication. Zhovtobryukh and Soares add:

Previously, customers building lakehouses chose between Iceberg tables in the Google-managed Iceberg REST catalog or tables managed by BigQuery based on their primary ETL engine. That means that customers relying on Apache Spark for ETL to Iceberg REST Catalog tables couldn’t write through BigQuery or use its storage management features.

The preview also includes centralized table access controls, allowing permissions to be managed consistently across query engines. With the latest announcements, Google Cloud now supports querying Iceberg data across AWS and Azure, interoperability with external platforms like Databricks and Snowflake, and integration with unstructured data and AI workflows.

BigQuery ObjectRefs are now generally available, allowing teams to combine structured Iceberg data with unstructured files stored in Cloud Storage for multimodal analysis and AI workflows. Additionally, Knowledge Catalog (formerly Dataplex), a governance layer currently in preview, manages metadata, lineage, and access controls across systems.

Practitioners discuss how the integration might remove the "hidden tax" on Iceberg adoption. David Colbert comments:

Teams get excited about Iceberg/Delta capabilities but hit friction fast on compaction, metadata management, and orchestration. The catalog point is key. Open formats solve storage portability, but control plane choices determine long-term optionality.

Reviewing the announcements from Next ‘26, Precious Pendo writes:

Google is betting that enterprise AI value will accrue to whoever owns the reasoning layer over data, not just the storage layer. AWS and Azure charge you for compute and storage. Google wants to charge you for context and intelligence.

Google Cloud is not the only provider focusing on Iceberg workloads, with AWS analytics services such as EMR, Glue, Athena, and Redshift providing native support for Iceberg. Discussing how Apache Iceberg is transforming modern data lakes, Shashank Muthuraj, cloud engineer at Red Oak Strategic, writes:

Apache Iceberg has moved from a Netflix engineering project to the undisputed standard for open data lakehouse architecture in less than seven years. The technical merits — ACID transactions, hidden partitioning, time travel, and engine independence — are compelling, but the real story is the unprecedented industry alignment.

While the core managed Iceberg table support in BigQuery is now generally available, the broader open interoperability and REST catalog capabilities announced at Iceberg Summit 2026 are still in preview.

About the Author

#### Renato Losio

Renato has extensive experience as a cloud architect, tech lead, and cloud services specialist. Currently, he lives in Berlin and works remotely as a principal cloud architect. His primary areas of interest include cloud services and relational databases. He is an editor at InfoQ and a recognized AWS Data Hero. You can connect with him on LinkedIn.

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