Google Cloud Introduces Cross-Engine Iceberg Support in BigQuery

2026-05-23 · Source: InfoQ · Field: Technology & Digital — Cloud Computing & IT Infrastructure, Data Science & Analytics, Artificial Intelligence & Machine Learning · Depth: Intermediate, medium

Summary

Google Cloud has introduced new interoperability features for Apache Iceberg in BigQuery, announced at the Apache Iceberg Summit last month. This includes a preview of a serverless Iceberg REST catalog, enabling teams to create, update, and query the same Apache Iceberg tables across BigQuery and other engines like Spark, Flink, and Trino without data duplication. The cloud provider also added managed support for metadata, table maintenance, and synchronization tasks. At Next '26, Google expanded this to cross-cloud lakehouse interoperability, supporting Iceberg catalog querying across AWS, Azure, Databricks, and Snowflake, alongside AI workflows. BigQuery ObjectRefs are now generally available for multimodal analysis with unstructured Cloud Storage files, and Knowledge Catalog is in preview for governance. This aims to reduce operational complexity and costs associated with Iceberg deployments while maintaining open data formats.

Key takeaway

For MLOps Engineers or Data Architects building multi-engine data lakehouses, Google Cloud's enhanced Iceberg support in BigQuery simplifies operations. You can now use a single Iceberg table across BigQuery, Spark, Flink, and Trino, reducing data duplication and management complexity. This also extends to cross-cloud querying and integrated AI workflows. Utilize the serverless Iceberg REST catalog and BigQuery ObjectRefs to streamline your data pipelines and governance, especially when integrating structured and unstructured data for AI.

Key insights

Google Cloud enhances BigQuery with cross-engine Apache Iceberg support, simplifying lakehouse operations and enabling multi-cloud, multi-tool data access.

Principles

• Open data formats enable tool independence.
• Managed services reduce lakehouse operational overhead.

In practice

• Combine structured Iceberg data with unstructured files.
• Manage permissions consistently across query engines.

Topics

• Google Cloud
• BigQuery
• Apache Iceberg
• Data Lakehouse
• Multi-Cloud
• Data Governance

Best for: CTO, VP of Engineering/Data, Director of AI/ML, Data Engineer, MLOps Engineer, AI Architect

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Editorial summary, takeaway, and curation by AIssential. Original article published by InfoQ.