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Cloud & Infrastructure
Google Cloud Blog·2d ago·5 min read
Cross-cloud conversational analytics made simple with open standards
TL;DR
Google Cloud introduces a solution for cross-cloud data analytics using BigQuery and Apache Iceberg, allowing organizations to query data in place without migration. This approach simplifies data access and enhances reporting capabilities across multiple platforms.
✦ Why It Matters
Start using BigQuery with Apache Iceberg to streamline your cross-cloud data analytics today.
Key Takeaways
- Implement federated queries to access data across multiple clouds.
- Utilize conversational analytics in BigQuery for natural language data exploration.
- Set up metadata synchronization to keep data schemas updated.
- Consider caching strategies to optimize query performance and reduce costs.
How It Works
The borderless Lakehouse architecture allows users to query data directly from its source without migration. It uses the Apache Iceberg REST catalog to synchronize metadata from various cloud platforms, enabling federated queries.
During execution, BigQuery processes these queries in place, reading data directly from remote storage, which eliminates the need for complex data pipelines.