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. By Renato Losio
The proliferation of data lakes and the increasing need for data interoperability across diverse compute engines drive the demand for solutions like cross-engine Iceberg support. Google is responding to market needs and Apache Iceberg's growing adoption.
This development significantly enhances data portability and reduces vendor lock-in for data lake users, allowing organizations to leverage their data assets more flexibly across various platforms including BigQuery, Spark, Flink, and Trino. It streamlines data architecture, reduces operational complexity, and facilitates more efficient data analysis and AI/ML workloads.
Data professionals can now use a single Iceberg table definition to access and manipulate data from different query engines without creating data copies, thereby improving data consistency, simplifying governance, and reducing storage costs.
- · Google Cloud
- · Data Engineers
- · Organizations with hybrid cloud data strategies
- · Apache Iceberg
- · Proprietary data catalog vendors
- · Cloud providers without strong open-table format support
Increased adoption of Apache Iceberg as a universal data table format across enterprise data architectures.
Accelerated migration of legacy data warehouses to modern data lakehouses leveraging open table formats, enabling more agile analytics and AI development.
Enhanced competition among cloud providers to offer superior interoperability and open-source data ecosystem integration, potentially leading to more unified data platforms and reduced data silos.
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